diff --git a/0_python/1_Basics.ipynb b/0_python/1_Basics.ipynb index 467f226..ae2c7e9 100644 --- a/0_python/1_Basics.ipynb +++ b/0_python/1_Basics.ipynb @@ -11,6 +11,30 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, + "outputs": [], + "source": [ + "a = 10\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## import\n", + "\n", + "```\n", + "import os\n", + "```\n", + "\n", + "$$\n", + "f(x) = sin(x)\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -60,7 +84,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -71,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -95,7 +119,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -104,7 +128,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -210,7 +234,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -219,7 +243,7 @@ "0.5" ] }, - "execution_count": 13, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -345,7 +369,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -354,7 +378,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -363,7 +387,7 @@ "True" ] }, - "execution_count": 18, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -374,7 +398,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -383,7 +407,7 @@ "False" ] }, - "execution_count": 19, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -601,7 +625,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -655,7 +679,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -664,7 +688,7 @@ "'b'" ] }, - "execution_count": 32, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -675,7 +699,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -684,7 +708,7 @@ "98" ] }, - "execution_count": 33, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -709,7 +733,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 12, "metadata": { "scrolled": false }, @@ -789,7 +813,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -873,7 +897,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -908,14 +932,14 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Type something here and it will be stored in variable abc \taa\n" + "Type something here and it will be stored in variable abc \tHello world!\n" ] } ], @@ -925,7 +949,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -934,7 +958,7 @@ "str" ] }, - "execution_count": 3, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } diff --git a/0_python/2_Print_Statement.ipynb b/0_python/2_Print_Statement.ipynb index 1e0549d..e7322e8 100644 --- a/0_python/2_Print_Statement.ipynb +++ b/0_python/2_Print_Statement.ipynb @@ -64,7 +64,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -92,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "metadata": { "scrolled": true }, @@ -123,7 +123,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -147,7 +147,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -180,7 +180,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -212,7 +212,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -243,24 +243,24 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "I want %d to be printed here\n" + "I want to be printed here\n" ] } ], "source": [ - "print(\"I want %%d to be printed %s\" %'here')" + "print(\"I want to be printed %s\" %'here')" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -277,7 +277,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -301,7 +301,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -325,7 +325,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -342,19 +342,15 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 18, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Routine:\n", - "\t- Eat\n", - "\t- Sleep\n", - "\t- Repeat\n", - "\n" + "ename": "SyntaxError", + "evalue": "Missing parentheses in call to 'print' (, line 5)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m5\u001b[0m\n\u001b[0;31m \"\"\"\u001b[0m\n\u001b[0m \n^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m Missing parentheses in call to 'print'\n" ] } ], @@ -384,7 +380,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -393,7 +389,7 @@ "'3.121312'" ] }, - "execution_count": 13, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -411,7 +407,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -420,7 +416,7 @@ "'3.12131'" ] }, - "execution_count": 14, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -438,7 +434,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -447,7 +443,7 @@ "' 3.12131'" ] }, - "execution_count": 15, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } diff --git a/0_python/3_Data_Structure_1.ipynb b/0_python/3_Data_Structure_1.ipynb index 866effd..1110741 100644 --- a/0_python/3_Data_Structure_1.ipynb +++ b/0_python/3_Data_Structure_1.ipynb @@ -65,7 +65,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -151,7 +151,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -167,7 +167,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -185,7 +185,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -194,7 +194,7 @@ "'orange'" ] }, - "execution_count": 9, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -215,7 +215,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -240,7 +240,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -249,7 +249,7 @@ "'apple'" ] }, - "execution_count": 12, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -310,7 +310,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -332,7 +332,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -358,7 +358,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -367,7 +367,7 @@ "[0, 3, 6]" ] }, - "execution_count": 6, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -392,7 +392,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -401,7 +401,7 @@ "10" ] }, - "execution_count": 7, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -419,7 +419,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -428,7 +428,7 @@ "0" ] }, - "execution_count": 19, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -439,7 +439,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -448,7 +448,7 @@ "9" ] }, - "execution_count": 20, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -466,7 +466,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -475,7 +475,7 @@ "[1, 2, 3, 5, 4, 7]" ] }, - "execution_count": 21, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -493,7 +493,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -509,7 +509,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -518,7 +518,7 @@ "False" ] }, - "execution_count": 23, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -529,7 +529,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -538,7 +538,7 @@ "True" ] }, - "execution_count": 24, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -549,7 +549,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -558,7 +558,7 @@ "False" ] }, - "execution_count": 26, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -596,7 +596,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -605,7 +605,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -631,7 +631,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -640,7 +640,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -673,7 +673,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -703,7 +703,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -712,7 +712,7 @@ "['h', 'e', 'l', 'l', 'o']" ] }, - "execution_count": 23, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -730,7 +730,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -739,7 +739,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -764,7 +764,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -773,7 +773,7 @@ "3" ] }, - "execution_count": 10, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -791,7 +791,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -800,7 +800,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -825,7 +825,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -850,7 +850,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -859,7 +859,7 @@ "0" ] }, - "execution_count": 14, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -870,7 +870,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -880,7 +880,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m999\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m999\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mValueError\u001b[0m: 999 is not in list" ] } @@ -898,7 +898,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -948,16 +948,16 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[1, 1, 4, 8, 7, 'Python', 1, [5, 4, 2, 8], 5, 4, 2]" + "[1, 1, 4, 8, 7, 'name', 1, [5, 4, 2, 8], 5, 4]" ] }, - "execution_count": 19, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -976,16 +976,16 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "8" + "4" ] }, - "execution_count": 22, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -996,14 +996,14 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[1, 4, 8, 7, 'Python', 1, [5, 4, 2, 8], 5, 4, 2]\n" + "[1, 1, 8, 7, 'name', 1, [5, 4, 2, 8], 5, 4]\n" ] } ], @@ -1020,19 +1020,19 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[1, 4, 7, 1, [5, 4, 2, 8], 5, 4, 2]\n" + "[1, 1, 8, 7, 1, [5, 4, 2, 8], 5, 4]\n" ] } ], "source": [ - "lst.remove('Python')\n", + "lst.remove('name')\n", "print(lst)" ] }, @@ -1072,14 +1072,14 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[2, 4, 5, [5, 4, 2, 8], 1, 7, 1]\n" + "[4, 5, [5, 4, 2, 8], 1, 7, 8, 1, 1]\n" ] } ], @@ -1099,7 +1099,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 43, "metadata": {}, "outputs": [ { @@ -1125,7 +1125,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -1150,7 +1150,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -1179,7 +1179,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -1214,7 +1214,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ @@ -1223,7 +1223,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -1248,7 +1248,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 49, "metadata": {}, "outputs": [ { @@ -1269,7 +1269,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 50, "metadata": {}, "outputs": [ { @@ -1374,7 +1374,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 51, "metadata": {}, "outputs": [ { @@ -1408,7 +1408,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ @@ -1425,7 +1425,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 53, "metadata": {}, "outputs": [ { @@ -1434,7 +1434,7 @@ "(27,)" ] }, - "execution_count": 41, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" } @@ -1452,7 +1452,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 54, "metadata": {}, "outputs": [ { @@ -1461,7 +1461,7 @@ "(27, 27)" ] }, - "execution_count": 42, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } @@ -1479,7 +1479,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 55, "metadata": { "scrolled": true }, @@ -1509,7 +1509,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 56, "metadata": {}, "outputs": [ { @@ -1536,7 +1536,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 57, "metadata": {}, "outputs": [], "source": [ @@ -1562,7 +1562,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 58, "metadata": {}, "outputs": [ { @@ -1594,9 +1594,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 59, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "d.count('a')" ] @@ -1610,9 +1621,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 60, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "d.index('a')" ] @@ -1635,7 +1657,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 61, "metadata": {}, "outputs": [ { @@ -1653,7 +1675,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 62, "metadata": {}, "outputs": [ { @@ -1685,7 +1707,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 63, "metadata": {}, "outputs": [], "source": [ @@ -1694,7 +1716,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 64, "metadata": {}, "outputs": [], "source": [ @@ -1710,7 +1732,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 65, "metadata": {}, "outputs": [ { @@ -1719,7 +1741,7 @@ "{1, 2, 3, 4, 5}" ] }, - "execution_count": 52, + "execution_count": 65, "metadata": {}, "output_type": "execute_result" } @@ -1737,7 +1759,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 66, "metadata": {}, "outputs": [ { @@ -1753,7 +1775,7 @@ "{0, 1, 2, 3}" ] }, - "execution_count": 56, + "execution_count": 66, "metadata": {}, "output_type": "execute_result" } @@ -1773,7 +1795,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 67, "metadata": {}, "outputs": [ { @@ -1782,7 +1804,7 @@ "{2, 3}" ] }, - "execution_count": 57, + "execution_count": 67, "metadata": {}, "output_type": "execute_result" } @@ -1800,7 +1822,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 68, "metadata": {}, "outputs": [ { @@ -1809,7 +1831,7 @@ "{0, 1}" ] }, - "execution_count": 58, + "execution_count": 68, "metadata": {}, "output_type": "execute_result" } diff --git a/0_python/4_Data_Structure_2.ipynb b/0_python/4_Data_Structure_2.ipynb index b55c6bf..2712347 100644 --- a/0_python/4_Data_Structure_2.ipynb +++ b/0_python/4_Data_Structure_2.ipynb @@ -16,9 +16,17 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10\n" + ] + } + ], "source": [ "String0 = 'Taj Mahal is beautiful'\n", "String1 = \"Taj Mahal is beautiful\"\n", @@ -29,7 +37,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -59,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -92,7 +100,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -118,7 +126,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -142,7 +150,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -168,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -193,7 +201,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -202,7 +210,7 @@ "' Taj Mahal is beautiful '" ] }, - "execution_count": 8, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -220,7 +228,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -229,7 +237,7 @@ "'------------------------Taj Mahal is beautiful------------------------'" ] }, - "execution_count": 9, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -247,7 +255,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -256,7 +264,7 @@ "'00000000Taj Mahal is beautiful'" ] }, - "execution_count": 10, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -274,7 +282,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -305,7 +313,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -323,7 +331,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mString0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Taj'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mString0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Mahal'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mString0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Mahal'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mString0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Taj'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mString0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Mahal'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mString0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Mahal'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mValueError\u001b[0m: substring not found" ] } @@ -343,7 +351,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -658,7 +666,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -674,7 +682,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -683,7 +691,7 @@ "'hello'" ] }, - "execution_count": 24, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -701,7 +709,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -710,7 +718,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -719,7 +727,7 @@ "' ***----hello---******* '" ] }, - "execution_count": 26, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -737,7 +745,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -803,7 +811,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -829,7 +837,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -848,14 +856,14 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'key1': 1, 3: (1, 4, 6), 'key2': [1, 2, 4]}\n" + "{'key2': [1, 2, 4], 'key1': 1, 3: (1, 4, 6)}\n" ] } ], @@ -873,7 +881,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -897,7 +905,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -914,14 +922,14 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'One': 1, 'Five': 5, 'Four': 4, 'Three': 3, 'Two': 2}\n" + "{'One': 1, 'Four': 4, 'Three': 3, 'Five': 5, 'Two': 2}\n" ] } ], @@ -941,18 +949,20 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{}\n" + "{'One': 1, 'Four': 4, 'Three': 3, 'Five': 5, 'Two': 2}\n" ] } ], "source": [ + "d2 = zip(names,numbers)\n", + "\n", "a1 = dict(d2)\n", "print(a1)" ] @@ -973,7 +983,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -998,14 +1008,32 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'One': 1, 'Four': 4, 'Three': 3, 'Five': 5, 'Two': 2}\n" + ] + } + ], + "source": [ + "a1 = {names[i]:numbers[i] for i in range(len(names))}\n", + "print(a1)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'One': 1, 'Five': 5, 'Four': 4, 'Three': 3, 'Two': 2}\n" + "{'One': 1, 'Four': 4, 'Three': 3, 'Five': 5, 'Two': 2}\n" ] } ], @@ -1024,16 +1052,16 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "dict_values([1, 5, 4, 3, 2])" + "dict_values([1, 4, 3, 5, 2])" ] }, - "execution_count": 38, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -1051,16 +1079,16 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "dict_keys(['One', 'Five', 'Four', 'Three', 'Two'])" + "dict_keys(['One', 'Four', 'Three', 'Five', 'Two'])" ] }, - "execution_count": 39, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -1078,7 +1106,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -1086,9 +1114,9 @@ "output_type": "stream", "text": [ "[ One] 1\n", - "[ Five] 5\n", "[ Four] 4\n", "[ Three] 3\n", + "[ Five] 5\n", "[ Two] 2\n" ] } @@ -1109,14 +1137,14 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'One': 1, 'Five': 5, 'Three': 3, 'Two': 2}\n", + "{'One': 1, 'Three': 3, 'Five': 5, 'Two': 2}\n", "4\n" ] } diff --git a/0_python/5_Control_Flow.ipynb b/0_python/5_Control_Flow.ipynb index cca26a5..bdb7aef 100644 --- a/0_python/5_Control_Flow.ipynb +++ b/0_python/5_Control_Flow.ipynb @@ -25,21 +25,23 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Hello\n" + "Welcome!\n" ] } ], "source": [ - "x = 12\n", + "x = 4\n", "if x >10:\n", - " print(\"Hello\")" + " print(\"Hello\")\n", + "else:\n", + " print(\"Welcome!\")" ] }, { @@ -271,7 +273,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -281,12 +283,15 @@ "1\n", "2\n", "3\n", + "\n", "4\n", "5\n", "6\n", + "\n", "7\n", "8\n", - "9\n" + "9\n", + "\n" ] } ], @@ -294,7 +299,8 @@ "list_of_lists = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]\n", "for list1 in list_of_lists:\n", " for x in list1:\n", - " print(x)" + " print(x)\n", + " print()" ] }, { @@ -315,7 +321,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -410,7 +416,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -455,7 +461,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -483,7 +489,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -492,7 +498,7 @@ "[27, 54, 81, 108, 135, 162, 189, 216, 243, 270]" ] }, - "execution_count": 15, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -517,7 +523,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -526,7 +532,7 @@ "[27, 54, 81, 108, 135, 162, 189, 216, 243, 270]" ] }, - "execution_count": 16, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -537,7 +543,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 13, "metadata": { "scrolled": true }, @@ -557,7 +563,7 @@ " '81': 81}" ] }, - "execution_count": 17, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -568,7 +574,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -577,7 +583,7 @@ "(27, 54, 81, 108, 135, 162, 189, 216, 243, 270)" ] }, - "execution_count": 18, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -595,22 +601,71 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[27, 54, 81, 108, 135, 162, 189, 216, 243, 270]" + "[1,\n", + " 2,\n", + " 3,\n", + " 4,\n", + " 5,\n", + " 6,\n", + " 7,\n", + " 8,\n", + " 9,\n", + " 10,\n", + " 28,\n", + " 29,\n", + " 30,\n", + " 31,\n", + " 32,\n", + " 33,\n", + " 34,\n", + " 35,\n", + " 36,\n", + " 37,\n", + " 55,\n", + " 56,\n", + " 57,\n", + " 58,\n", + " 59,\n", + " 60,\n", + " 61,\n", + " 62,\n", + " 63,\n", + " 64,\n", + " 82,\n", + " 83,\n", + " 84,\n", + " 85,\n", + " 86,\n", + " 87,\n", + " 88,\n", + " 89,\n", + " 90,\n", + " 91,\n", + " 109,\n", + " 110,\n", + " 111,\n", + " 112,\n", + " 113,\n", + " 114,\n", + " 115,\n", + " 116,\n", + " 117,\n", + " 118]" ] }, - "execution_count": 19, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "[27*z for i in range(50) if i==27 for z in range(1,11)]" + "[27*i+z for i in range(50) if i<5 for z in range(1,11)]" ] } ], diff --git a/0_python/6_Function.ipynb b/0_python/6_Function.ipynb index 56a3a08..56423aa 100644 --- a/0_python/6_Function.ipynb +++ b/0_python/6_Function.ipynb @@ -74,7 +74,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -85,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -114,7 +114,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -125,14 +125,14 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Please enter your name : Bu\n" + "Please enter your name : Willam\n" ] } ], @@ -149,15 +149,15 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Hey Bu!\n", - "Bu, How do you do?\n" + "Hey Willam!\n", + "Willam, How do you do?\n" ] } ], @@ -221,7 +221,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -239,7 +239,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -271,7 +271,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -282,7 +282,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -307,7 +307,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -328,7 +328,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -344,7 +344,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -362,7 +362,7 @@ " lowest = min(eglist)\n", " first = eglist[0]\n", " last = eglist[-1]\n", - " return (highest,lowest,first,last)" + " return highest,lowest,first,last" ] }, { @@ -392,7 +392,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -427,7 +427,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -451,7 +451,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -460,7 +460,7 @@ "7" ] }, - "execution_count": 22, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -478,7 +478,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -487,7 +487,7 @@ "8" ] }, - "execution_count": 19, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -532,7 +532,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -554,7 +554,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -570,7 +570,7 @@ "15" ] }, - "execution_count": 25, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -607,6 +607,41 @@ ] }, { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[30, 10, 20]\n" + ] + }, + { + "data": { + "text/plain": [ + "60" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def add_nd(**kwargs):\n", + " res = 0\n", + " reslist = []\n", + " for (k,v) in kwargs.items():\n", + " reslist.append(v)\n", + " print(reslist)\n", + " return sum(reslist)\n", + "\n", + "add_nd(x=10, y=20, c=30)" + ] + }, + { "cell_type": "markdown", "metadata": {}, "source": [ @@ -622,7 +657,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -638,7 +673,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -650,8 +685,8 @@ " print(\"This is happening inside the function :\", eg2)\n", " \n", " # what's the difference between following two lines?\n", - " eg1.append(7)\n", - " #eg1 = [1, 3, 5, 6]\n", + " #eg1.append(7)\n", + " eg1 = [1, 2, 3, 4, 5, 7]\n", " \n", " print(\"This is happening before the function is called : \", eg1)\n", " thirdfunc(eg1)\n", @@ -662,7 +697,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 43, "metadata": {}, "outputs": [ { @@ -672,7 +707,7 @@ "This is happening before the function is called : [1, 2, 3, 4, 5, 7]\n", "This is happening inside the function : [1, 2, 3, 4, 5, 7, 6]\n", "This is happening outside the function : [1, 2, 3, 4, 5, 7]\n", - "[1, 2, 3, 4, 5, 7]\n" + "[1, 2, 3, 4, 5]\n" ] } ], @@ -707,11 +742,18 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":8: SyntaxWarning: name 'eg1' is used prior to global declaration\n", + " global eg1\n" + ] + } + ], "source": [ "def egfunc1():\n", " def thirdfunc(arg1):\n", @@ -720,6 +762,8 @@ " eg2.append(6)\n", " print(\"This is happening inside the function :\", eg2)\n", " print(\"This is happening before the function is called : \", eg1)\n", + " global eg1\n", + " eg1 = [1, 2, 3, 4, 5, 7]\n", " thirdfunc(eg1)\n", " print(\"This is happening outside the function :\", eg1) \n", " print(\"Accessing a variable declared inside the function from outside :\" , eg2)" @@ -761,7 +805,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -770,7 +814,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 49, "metadata": {}, "outputs": [ { @@ -779,7 +823,7 @@ "64" ] }, - "execution_count": 36, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" } @@ -790,7 +834,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 50, "metadata": {}, "outputs": [ { @@ -799,7 +843,7 @@ "(6, 8)" ] }, - "execution_count": 40, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" } @@ -811,7 +855,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 51, "metadata": {}, "outputs": [ { @@ -820,7 +864,7 @@ "function" ] }, - "execution_count": 42, + "execution_count": 51, "metadata": {}, "output_type": "execute_result" } @@ -831,7 +875,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 52, "metadata": {}, "outputs": [ { @@ -840,7 +884,7 @@ "function" ] }, - "execution_count": 43, + "execution_count": 52, "metadata": {}, "output_type": "execute_result" } @@ -868,7 +912,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 53, "metadata": {}, "outputs": [], "source": [ @@ -877,7 +921,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 54, "metadata": {}, "outputs": [ { @@ -895,7 +939,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 55, "metadata": {}, "outputs": [ { @@ -986,7 +1030,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 56, "metadata": {}, "outputs": [], "source": [ @@ -1002,7 +1046,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 57, "metadata": {}, "outputs": [ { @@ -1027,16 +1071,16 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[True, True, True, True, False, False, False, False, False]" + "" ] }, - "execution_count": 39, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" } @@ -1054,16 +1098,16 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[4, 8]" + "" ] }, - "execution_count": 40, + "execution_count": 59, "metadata": {}, "output_type": "execute_result" } diff --git a/0_python/7_Class.ipynb b/0_python/7_Class.ipynb index fa7dce7..1db0b34 100644 --- a/0_python/7_Class.ipynb +++ b/0_python/7_Class.ipynb @@ -32,12 +32,12 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "class FirstClass:\n", - " pass" + " pass\n" ] }, { @@ -56,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -65,7 +65,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -74,7 +74,7 @@ "__main__.FirstClass" ] }, - "execution_count": 4, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -85,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -94,7 +94,7 @@ "type" ] }, - "execution_count": 5, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -128,7 +128,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -138,7 +138,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0meg0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFirstClass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0meg0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0meg0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFirstClass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0meg0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m: 'FirstClass' object has no attribute 'init'" ] } @@ -166,7 +166,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -186,7 +186,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -196,21 +196,23 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "one 1\n", - "two 2\n" + "onex 11\n", + "two 2\n", + "My first class\n" ] } ], "source": [ "print(eg1.name, eg1.symbol)\n", - "print(eg2.name, eg2.symbol)" + "print(eg2.name, eg2.symbol)\n", + "print(eg1.__doc__)" ] }, { @@ -222,7 +224,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 16, "metadata": { "scrolled": false }, @@ -257,7 +259,7 @@ " '__weakref__']" ] }, - "execution_count": 22, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -348,7 +350,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -367,7 +369,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -377,7 +379,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -387,7 +389,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meg1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meg1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msymbol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meg2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meg2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msymbol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meg1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meg1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msymbol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meg2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meg2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msymbol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mAttributeError\u001b[0m: 'FirstClass' object has no attribute 'name'" ] } @@ -406,7 +408,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -441,7 +443,7 @@ " 's']" ] }, - "execution_count": 29, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -534,10 +536,8 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": true - }, + "execution_count": 25, + "metadata": {}, "outputs": [], "source": [ "eg1.cube = 1\n", @@ -546,16 +546,43 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['__doc__', '__init__', '__module__', 'cube', 'n', 's']" + "['__class__',\n", + " '__delattr__',\n", + " '__dict__',\n", + " '__dir__',\n", + " '__doc__',\n", + " '__eq__',\n", + " '__format__',\n", + " '__ge__',\n", + " '__getattribute__',\n", + " '__gt__',\n", + " '__hash__',\n", + " '__init__',\n", + " '__le__',\n", + " '__lt__',\n", + " '__module__',\n", + " '__ne__',\n", + " '__new__',\n", + " '__reduce__',\n", + " '__reduce_ex__',\n", + " '__repr__',\n", + " '__setattr__',\n", + " '__sizeof__',\n", + " '__str__',\n", + " '__subclasshook__',\n", + " '__weakref__',\n", + " 'cube',\n", + " 'n',\n", + " 's']" ] }, - "execution_count": 20, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -579,7 +606,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -599,7 +626,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -619,7 +646,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -643,10 +670,8 @@ }, { "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": true - }, + "execution_count": 34, + "metadata": {}, "outputs": [], "source": [ "class FirstClass:\n", @@ -663,10 +688,8 @@ }, { "cell_type": "code", - "execution_count": 25, - "metadata": { - "collapsed": true - }, + "execution_count": 35, + "metadata": {}, "outputs": [], "source": [ "eg4 = FirstClass('Five',5)" @@ -674,7 +697,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -687,8 +710,8 @@ } ], "source": [ - "print eg4.square()\n", - "print eg4.cube()" + "print(eg4.square())\n", + "print(eg4.cube())" ] }, { @@ -761,10 +784,8 @@ }, { "cell_type": "code", - "execution_count": 29, - "metadata": { - "collapsed": true - }, + "execution_count": 38, + "metadata": {}, "outputs": [], "source": [ "class SoftwareEngineer:\n", @@ -778,7 +799,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -787,7 +808,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -804,16 +825,41 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['__doc__', '__init__', '__module__', 'salary']" + "['__class__',\n", + " '__delattr__',\n", + " '__dict__',\n", + " '__dir__',\n", + " '__doc__',\n", + " '__eq__',\n", + " '__format__',\n", + " '__ge__',\n", + " '__getattribute__',\n", + " '__gt__',\n", + " '__hash__',\n", + " '__init__',\n", + " '__le__',\n", + " '__lt__',\n", + " '__module__',\n", + " '__ne__',\n", + " '__new__',\n", + " '__reduce__',\n", + " '__reduce_ex__',\n", + " '__repr__',\n", + " '__setattr__',\n", + " '__sizeof__',\n", + " '__str__',\n", + " '__subclasshook__',\n", + " '__weakref__',\n", + " 'salary']" ] }, - "execution_count": 32, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -831,7 +877,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -839,7 +885,7 @@ " def __init__(self,name,age):\n", " self.name = name\n", " self.age = age\n", - " def money(self,value):\n", + " def salary(self,value):\n", " self.money = value\n", " print(self.name,\"earns\",self.money)\n", " def artform(self, job):\n", @@ -849,10 +895,8 @@ }, { "cell_type": "code", - "execution_count": 34, - "metadata": { - "collapsed": true - }, + "execution_count": 43, + "metadata": {}, "outputs": [], "source": [ "b = Artist('Nitin',20)" @@ -860,7 +904,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -873,22 +917,48 @@ } ], "source": [ - "b.money(50000)\n", + "b.salary(50000)\n", "b.artform('Musician')" ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['__doc__', '__init__', '__module__', 'artform', 'money']" + "['__class__',\n", + " '__delattr__',\n", + " '__dict__',\n", + " '__dir__',\n", + " '__doc__',\n", + " '__eq__',\n", + " '__format__',\n", + " '__ge__',\n", + " '__getattribute__',\n", + " '__gt__',\n", + " '__hash__',\n", + " '__init__',\n", + " '__le__',\n", + " '__lt__',\n", + " '__module__',\n", + " '__ne__',\n", + " '__new__',\n", + " '__reduce__',\n", + " '__reduce_ex__',\n", + " '__repr__',\n", + " '__setattr__',\n", + " '__sizeof__',\n", + " '__str__',\n", + " '__subclasshook__',\n", + " '__weakref__',\n", + " 'artform',\n", + " 'money']" ] }, - "execution_count": 36, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -906,7 +976,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -918,7 +988,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ @@ -927,16 +997,42 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['__doc__', '__init__', '__module__', 'artform', 'salary']" + "['__class__',\n", + " '__delattr__',\n", + " '__dict__',\n", + " '__dir__',\n", + " '__doc__',\n", + " '__eq__',\n", + " '__format__',\n", + " '__ge__',\n", + " '__getattribute__',\n", + " '__gt__',\n", + " '__hash__',\n", + " '__init__',\n", + " '__le__',\n", + " '__lt__',\n", + " '__module__',\n", + " '__ne__',\n", + " '__new__',\n", + " '__reduce__',\n", + " '__reduce_ex__',\n", + " '__repr__',\n", + " '__setattr__',\n", + " '__sizeof__',\n", + " '__str__',\n", + " '__subclasshook__',\n", + " '__weakref__',\n", + " 'artform',\n", + " 'salary']" ] }, - "execution_count": 39, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } @@ -947,7 +1043,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 49, "metadata": {}, "outputs": [ { @@ -973,10 +1069,8 @@ }, { "cell_type": "code", - "execution_count": 41, - "metadata": { - "collapsed": true - }, + "execution_count": 50, + "metadata": {}, "outputs": [], "source": [ "class Artist(SoftwareEngineer):\n", @@ -991,10 +1085,8 @@ }, { "cell_type": "code", - "execution_count": 42, - "metadata": { - "collapsed": true - }, + "execution_count": 51, + "metadata": {}, "outputs": [], "source": [ "c = Artist('Nishanth',21)" @@ -1002,7 +1094,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 52, "metadata": {}, "outputs": [ { @@ -1029,7 +1121,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 56, "metadata": {}, "outputs": [], "source": [ @@ -1046,7 +1138,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 57, "metadata": {}, "outputs": [], "source": [ @@ -1055,7 +1147,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 59, "metadata": {}, "outputs": [ { @@ -1068,7 +1160,7 @@ ], "source": [ "xc.one(1)\n", - "print xc.data" + "print(xc.data)" ] }, { @@ -1080,7 +1172,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 60, "metadata": {}, "outputs": [ { @@ -1093,12 +1185,12 @@ ], "source": [ "xc.data.append(8)\n", - "print xc.data" + "print(xc.data)" ] }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 61, "metadata": {}, "outputs": [ { @@ -1111,7 +1203,7 @@ ], "source": [ "xc.two(3)\n", - "print xc.data" + "print(xc.data)" ] }, { @@ -1123,7 +1215,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 62, "metadata": {}, "outputs": [], "source": [ @@ -1134,7 +1226,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 63, "metadata": {}, "outputs": [], "source": [ @@ -1143,7 +1235,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 64, "metadata": {}, "outputs": [ { @@ -1152,7 +1244,7 @@ "'IDoNotKnowWhatToType'" ] }, - "execution_count": 51, + "execution_count": 64, "metadata": {}, "output_type": "execute_result" } diff --git a/1_numpy_matplotlib_scipy_sympy/matplotlib_simple_tutorial.ipynb b/1_numpy_matplotlib_scipy_sympy/matplotlib_simple_tutorial.ipynb index 8c98e3e..91c18f4 100644 --- a/1_numpy_matplotlib_scipy_sympy/matplotlib_simple_tutorial.ipynb +++ b/1_numpy_matplotlib_scipy_sympy/matplotlib_simple_tutorial.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -48,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": { "lines_to_next_cell": 2 }, @@ -56,10 +56,10 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 3, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, @@ -89,7 +89,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -114,7 +114,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -138,7 +138,9 @@ "t = np.arange(0., 5., 0.2)\n", "\n", "# red dashes, blue squares and green triangles\n", - "plt.plot(t, t, 'r--', t, t**2, 'bs', t, t**3, 'g^')\n", + "plt.plot(t, t, 'r--', \\\n", + " t, t**2, 'bs', \\\n", + " t, t**3, 'g^')\n", "plt.show()" ] }, @@ -163,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -190,10 +192,10 @@ "t2 = np.arange(0.0, 5.0, 0.02)\n", "\n", "plt.figure(1)\n", - "plt.subplot(211)\n", + "plt.subplot(2,1,1)\n", "plt.plot(t1, f(t1), 'bo', t2, f(t2), 'k')\n", "\n", - "plt.subplot(212)\n", + "plt.subplot(2,1,2)\n", "plt.plot(t2, np.cos(2*np.pi*t2), 'r--')\n", "plt.show()" ] @@ -207,7 +209,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -243,16 +245,16 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 14, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, @@ -276,16 +278,16 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 15, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, @@ -317,7 +319,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -407,7 +409,7 @@ " )" ] }, - "execution_count": 16, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, diff --git a/1_numpy_matplotlib_scipy_sympy/numpy_tutorial.ipynb b/1_numpy_matplotlib_scipy_sympy/numpy_tutorial.ipynb index 3a766c6..09a6b9e 100644 --- a/1_numpy_matplotlib_scipy_sympy/numpy_tutorial.ipynb +++ b/1_numpy_matplotlib_scipy_sympy/numpy_tutorial.ipynb @@ -47,11 +47,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "from numpy import *" + "from numpy import *\n", + "import numpy as np" ] }, { @@ -96,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -105,7 +106,7 @@ "array([1, 2, 3, 4])" ] }, - "execution_count": 3, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -121,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -152,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -161,7 +162,7 @@ "(numpy.ndarray, numpy.ndarray)" ] }, - "execution_count": 11, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -179,7 +180,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -188,7 +189,7 @@ "(4,)" ] }, - "execution_count": 12, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -199,7 +200,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -208,7 +209,7 @@ "(3, 2)" ] }, - "execution_count": 13, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -253,7 +254,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -262,18 +263,18 @@ "(3, 2)" ] }, - "execution_count": 15, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "shape(M)" + "np.shape(M)" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -282,13 +283,13 @@ "6" ] }, - "execution_count": 16, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "size(M)" + "np.size(M)" ] }, { @@ -336,7 +337,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -346,7 +347,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mM\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"hello\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mM\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"hello\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: 'hello'" ] } @@ -364,7 +365,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -374,13 +375,13 @@ " [3.+0.j, 4.+0.j]])" ] }, - "execution_count": 22, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "M = array([[1, 2], [3, 4]], dtype=complex)\n", + "M = np.array([[1, 2], [3, 4]], dtype=complex)\n", "\n", "M" ] @@ -417,7 +418,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -432,7 +433,7 @@ "source": [ "# create a range\n", "\n", - "x = arange(0, 10, 1) # arguments: start, stop, step\n", + "x = np.arange(0, 10, 1) # arguments: start, stop, step\n", "y = range(0, 10, 1)\n", "print(x)\n", "print(list(y))" @@ -440,28 +441,26 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([ -1.00000000e+00, -9.00000000e-01, -8.00000000e-01,\n", - " -7.00000000e-01, -6.00000000e-01, -5.00000000e-01,\n", - " -4.00000000e-01, -3.00000000e-01, -2.00000000e-01,\n", - " -1.00000000e-01, -2.22044605e-16, 1.00000000e-01,\n", - " 2.00000000e-01, 3.00000000e-01, 4.00000000e-01,\n", - " 5.00000000e-01, 6.00000000e-01, 7.00000000e-01,\n", - " 8.00000000e-01, 9.00000000e-01])" + "array([-1.00000000e+00, -9.00000000e-01, -8.00000000e-01, -7.00000000e-01,\n", + " -6.00000000e-01, -5.00000000e-01, -4.00000000e-01, -3.00000000e-01,\n", + " -2.00000000e-01, -1.00000000e-01, -2.22044605e-16, 1.00000000e-01,\n", + " 2.00000000e-01, 3.00000000e-01, 4.00000000e-01, 5.00000000e-01,\n", + " 6.00000000e-01, 7.00000000e-01, 8.00000000e-01, 9.00000000e-01])" ] }, - "execution_count": 15, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "x = arange(-1, 1, 0.1)\n", + "x = np.arange(-1, 1, 0.1)\n", "\n", "x" ] @@ -475,51 +474,49 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([ 0. , 0.41666667, 0.83333333, 1.25 ,\n", - " 1.66666667, 2.08333333, 2.5 , 2.91666667,\n", - " 3.33333333, 3.75 , 4.16666667, 4.58333333,\n", - " 5. , 5.41666667, 5.83333333, 6.25 ,\n", - " 6.66666667, 7.08333333, 7.5 , 7.91666667,\n", - " 8.33333333, 8.75 , 9.16666667, 9.58333333, 10. ])" + "array([ 0. , 0.41666667, 0.83333333, 1.25 , 1.66666667,\n", + " 2.08333333, 2.5 , 2.91666667, 3.33333333, 3.75 ,\n", + " 4.16666667, 4.58333333, 5. , 5.41666667, 5.83333333,\n", + " 6.25 , 6.66666667, 7.08333333, 7.5 , 7.91666667,\n", + " 8.33333333, 8.75 , 9.16666667, 9.58333333, 10. ])" ] }, - "execution_count": 16, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# using linspace, both end points ARE included\n", - "linspace(0, 10, 25)" + "np.linspace(0, 10, 25)" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([ 1.00000000e+00, 3.03773178e+00, 9.22781435e+00,\n", - " 2.80316249e+01, 8.51525577e+01, 2.58670631e+02,\n", - " 7.85771994e+02, 2.38696456e+03, 7.25095809e+03,\n", - " 2.20264658e+04])" + "array([1.00000000e+00, 3.03773178e+00, 9.22781435e+00, 2.80316249e+01,\n", + " 8.51525577e+01, 2.58670631e+02, 7.85771994e+02, 2.38696456e+03,\n", + " 7.25095809e+03, 2.20264658e+04])" ] }, - "execution_count": 17, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "logspace(0, 10, 10, base=e)" + "np.logspace(0, 10, 10, base=e)" ] }, { @@ -531,16 +528,16 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ - "x, y = mgrid[0:5, 0:5] # similar to meshgrid in MATLAB" + "x, y = np.mgrid[0:5, 0:5] # similar to meshgrid in MATLAB" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -553,7 +550,7 @@ " [4, 4, 4, 4, 4]])" ] }, - "execution_count": 19, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -564,7 +561,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -577,7 +574,7 @@ " [0, 1, 2, 3, 4]])" ] }, - "execution_count": 20, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -595,7 +592,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -604,9 +601,24 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 30, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.82594014, 0.31160547, 0.77827738, 0.59082014, 0.69654657],\n", + " [0.64715318, 0.05551977, 0.38057657, 0.45135262, 0.37209654],\n", + " [0.01234335, 0.12906551, 0.75598568, 0.20905719, 0.86103339],\n", + " [0.62784645, 0.87732666, 0.96543239, 0.41053462, 0.87116428],\n", + " [0.44218283, 0.70837525, 0.15065753, 0.93552422, 0.79261749]])" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# uniform random numbers in [0,1)\n", "random.rand(5,5)" @@ -614,20 +626,20 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[-0.8468269 , -0.32187515, 1.520302 , -0.10714052, 1.88130228],\n", - " [-2.08707056, 0.1204091 , 0.0866864 , -0.46315531, 1.47835324],\n", - " [-0.22495755, 0.28529696, 0.1506852 , 0.89387502, 0.65961749],\n", - " [ 1.0274951 , -0.33844001, -0.01689332, 0.50706762, 0.13447227],\n", - " [-0.17262793, -0.29960596, -0.28114172, 0.37986187, -0.16023985]])" + "array([[ 0.69829709, 0.04679976, 0.95770162, 1.91007838, -0.41865049],\n", + " [ 0.51678337, -0.34692074, 2.19264774, -0.59725524, -1.15314406],\n", + " [ 0.03361378, -0.0054733 , -0.77389592, -0.12696594, 1.69339468],\n", + " [-0.13267375, 0.95688595, 0.28043241, 0.83043672, 0.62677072],\n", + " [-0.09168095, -0.25064829, 0.49440189, -1.18704973, -1.28781414]])" ] }, - "execution_count": 29, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -646,7 +658,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -657,19 +669,19 @@ " [0, 0, 3]])" ] }, - "execution_count": 30, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# a diagonal matrix\n", - "diag([1,2,3])" + "np.diag([1,2,3])" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -681,7 +693,7 @@ " [0, 0, 0, 0]])" ] }, - "execution_count": 31, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -700,7 +712,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -711,18 +723,18 @@ " [0., 0., 0.]])" ] }, - "execution_count": 32, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "zeros((3,3))" + "np.zeros((3,3))" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -733,13 +745,13 @@ " [1., 1., 1.]])" ] }, - "execution_count": 33, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "ones((3,3))" + "np.ones((3,3))" ] }, { @@ -765,7 +777,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -791,7 +803,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -801,7 +813,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -810,7 +822,7 @@ "(77431, 7)" ] }, - "execution_count": 2, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -821,7 +833,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -858,18 +870,18 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[0.05154414, 0.85499268, 0.11256926],\n", - " [0.37333703, 0.71399532, 0.91667067],\n", - " [0.55448257, 0.53279085, 0.49926659]])" + "array([[0.85030715, 0.33330859, 0.64002838],\n", + " [0.52521743, 0.21572812, 0.33287991],\n", + " [0.74605429, 0.35134767, 0.45873422]])" ] }, - "execution_count": 4, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -882,7 +894,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ @@ -891,16 +903,16 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "5.154413507058341892e-02 8.549926762495245747e-01 1.125692636334880703e-01\r\n", - "3.733370292698003912e-01 7.139953150984013064e-01 9.166706651790860194e-01\r\n", - "5.544825679398455165e-01 5.327908466251080055e-01 4.992665856784902489e-01\r\n" + "8.503071542574233144e-01 3.333085915891427220e-01 6.400283846962552259e-01\r\n", + "5.252174340396357222e-01 2.157281249144539226e-01 3.328799104985459278e-01\r\n", + "7.460542870039649221e-01 3.513476662217395186e-01 4.587342216214667090e-01\r\n" ] } ], @@ -910,16 +922,16 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 49, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0.05154 0.85499 0.11257\r\n", - "0.37334 0.71400 0.91667\r\n", - "0.55448 0.53279 0.49927\r\n" + "0.85031 0.33331 0.64003\r\n", + "0.52522 0.21573 0.33288\r\n", + "0.74605 0.35135 0.45873\r\n" ] } ], @@ -945,7 +957,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 50, "metadata": {}, "outputs": [ { @@ -964,18 +976,18 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[0.05154414, 0.85499268, 0.11256926],\n", - " [0.37333703, 0.71399532, 0.91667067],\n", - " [0.55448257, 0.53279085, 0.49926659]])" + "array([[0.85030715, 0.33330859, 0.64002838],\n", + " [0.52521743, 0.21572812, 0.33287991],\n", + " [0.74605429, 0.35134767, 0.45873422]])" ] }, - "execution_count": 9, + "execution_count": 51, "metadata": {}, "output_type": "execute_result" } @@ -993,7 +1005,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 52, "metadata": {}, "outputs": [ { @@ -1012,7 +1024,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 53, "metadata": {}, "outputs": [ { @@ -1021,7 +1033,7 @@ "72" ] }, - "execution_count": 11, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" } @@ -1032,7 +1044,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 54, "metadata": {}, "outputs": [ { @@ -1041,7 +1053,7 @@ "2" ] }, - "execution_count": 12, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } @@ -1073,7 +1085,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 55, "metadata": {}, "outputs": [ { @@ -1082,7 +1094,7 @@ "1" ] }, - "execution_count": 14, + "execution_count": 55, "metadata": {}, "output_type": "execute_result" } @@ -1095,16 +1107,16 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 56, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0.7139953150984013\n", - "0.7139953150984013\n", - "[0.37333703 0.71399532 0.91667067]\n" + "0.21572812491445392\n", + "0.21572812491445392\n", + "[0.52521743 0.21572812 0.33287991]\n" ] } ], @@ -1125,18 +1137,18 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[0.05154414, 0.85499268, 0.11256926],\n", - " [0.37333703, 0.71399532, 0.91667067],\n", - " [0.55448257, 0.53279085, 0.49926659]])" + "array([[0.85030715, 0.33330859, 0.64002838],\n", + " [0.52521743, 0.21572812, 0.33287991],\n", + " [0.74605429, 0.35134767, 0.45873422]])" ] }, - "execution_count": 17, + "execution_count": 57, "metadata": {}, "output_type": "execute_result" } @@ -1147,16 +1159,16 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([0.37333703, 0.71399532, 0.91667067])" + "array([0.52521743, 0.21572812, 0.33287991])" ] }, - "execution_count": 18, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" } @@ -1174,16 +1186,16 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([0.37333703, 0.71399532, 0.91667067])" + "array([0.52521743, 0.21572812, 0.33287991])" ] }, - "execution_count": 19, + "execution_count": 59, "metadata": {}, "output_type": "execute_result" } @@ -1194,16 +1206,16 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([0.85499268, 0.71399532, 0.53279085])" + "array([0.33330859, 0.21572812, 0.35134767])" ] }, - "execution_count": 20, + "execution_count": 60, "metadata": {}, "output_type": "execute_result" } @@ -1221,7 +1233,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 61, "metadata": {}, "outputs": [], "source": [ @@ -1230,18 +1242,18 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[1. , 0.85499268, 0.11256926],\n", - " [0.37333703, 0.71399532, 0.91667067],\n", - " [0.55448257, 0.53279085, 0.49926659]])" + "array([[1. , 0.33330859, 0.64002838],\n", + " [0.52521743, 0.21572812, 0.33287991],\n", + " [0.74605429, 0.35134767, 0.45873422]])" ] }, - "execution_count": 22, + "execution_count": 62, "metadata": {}, "output_type": "execute_result" } @@ -1252,7 +1264,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 64, "metadata": {}, "outputs": [], "source": [ @@ -1299,7 +1311,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 65, "metadata": {}, "outputs": [ { @@ -1308,7 +1320,7 @@ "array([1, 2, 3, 4, 5])" ] }, - "execution_count": 26, + "execution_count": 65, "metadata": {}, "output_type": "execute_result" } @@ -1320,7 +1332,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 66, "metadata": {}, "outputs": [ { @@ -1329,7 +1341,7 @@ "array([2, 3])" ] }, - "execution_count": 27, + "execution_count": 66, "metadata": {}, "output_type": "execute_result" } @@ -1347,7 +1359,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 67, "metadata": {}, "outputs": [ { @@ -1356,7 +1368,7 @@ "array([ 1, -2, -3, 4, 5])" ] }, - "execution_count": 30, + "execution_count": 67, "metadata": {}, "output_type": "execute_result" } @@ -1377,7 +1389,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 68, "metadata": {}, "outputs": [ { @@ -1386,7 +1398,7 @@ "array([ 1, -2, -3, 4, 5])" ] }, - "execution_count": 31, + "execution_count": 68, "metadata": {}, "output_type": "execute_result" } @@ -1397,7 +1409,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 69, "metadata": {}, "outputs": [ { @@ -1406,7 +1418,7 @@ "array([ 1, -2, -3, 4, 5])" ] }, - "execution_count": 33, + "execution_count": 69, "metadata": {}, "output_type": "execute_result" } @@ -1417,7 +1429,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 70, "metadata": {}, "outputs": [ { @@ -1426,7 +1438,7 @@ "array([ 1, -3, 5])" ] }, - "execution_count": 55, + "execution_count": 70, "metadata": {}, "output_type": "execute_result" } @@ -1437,7 +1449,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 71, "metadata": {}, "outputs": [ { @@ -1446,7 +1458,7 @@ "array([ 1, -2, -3])" ] }, - "execution_count": 56, + "execution_count": 71, "metadata": {}, "output_type": "execute_result" } @@ -1457,7 +1469,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 72, "metadata": {}, "outputs": [ { @@ -1466,7 +1478,7 @@ "array([4, 5])" ] }, - "execution_count": 57, + "execution_count": 72, "metadata": {}, "output_type": "execute_result" } @@ -1484,7 +1496,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 73, "metadata": {}, "outputs": [], "source": [ @@ -1493,7 +1505,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 74, "metadata": {}, "outputs": [ { @@ -1502,7 +1514,7 @@ "5" ] }, - "execution_count": 35, + "execution_count": 74, "metadata": {}, "output_type": "execute_result" } @@ -1513,7 +1525,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 75, "metadata": {}, "outputs": [ { @@ -1522,7 +1534,7 @@ "array([3, 4, 5])" ] }, - "execution_count": 36, + "execution_count": 75, "metadata": {}, "output_type": "execute_result" } @@ -1540,7 +1552,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 76, "metadata": {}, "outputs": [ { @@ -1553,7 +1565,7 @@ " [40, 41, 42, 43, 44]])" ] }, - "execution_count": 38, + "execution_count": 76, "metadata": {}, "output_type": "execute_result" } @@ -1566,7 +1578,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 77, "metadata": {}, "outputs": [ { @@ -1577,7 +1589,7 @@ " [31, 32, 33]])" ] }, - "execution_count": 62, + "execution_count": 77, "metadata": {}, "output_type": "execute_result" } @@ -1626,7 +1638,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 78, "metadata": {}, "outputs": [ { @@ -1652,7 +1664,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 79, "metadata": {}, "outputs": [ { @@ -1661,7 +1673,7 @@ "array([11, 22, 34])" ] }, - "execution_count": 65, + "execution_count": 79, "metadata": {}, "output_type": "execute_result" } @@ -1680,7 +1692,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 80, "metadata": {}, "outputs": [ { @@ -1689,7 +1701,7 @@ "array([0, 1, 2, 3, 4])" ] }, - "execution_count": 66, + "execution_count": 80, "metadata": {}, "output_type": "execute_result" } @@ -1701,7 +1713,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 81, "metadata": {}, "outputs": [ { @@ -1710,7 +1722,7 @@ "array([0, 2])" ] }, - "execution_count": 67, + "execution_count": 81, "metadata": {}, "output_type": "execute_result" } @@ -1722,7 +1734,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 82, "metadata": {}, "outputs": [ { @@ -1731,7 +1743,7 @@ "array([0, 2])" ] }, - "execution_count": 68, + "execution_count": 82, "metadata": {}, "output_type": "execute_result" } @@ -1751,7 +1763,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 84, "metadata": {}, "outputs": [ { @@ -1761,7 +1773,7 @@ " 6.5, 7. , 7.5, 8. , 8.5, 9. , 9.5])" ] }, - "execution_count": 42, + "execution_count": 84, "metadata": {}, "output_type": "execute_result" } @@ -1773,7 +1785,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 86, "metadata": {}, "outputs": [ { @@ -1784,7 +1796,7 @@ " False, False])" ] }, - "execution_count": 44, + "execution_count": 86, "metadata": {}, "output_type": "execute_result" } @@ -1797,7 +1809,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 87, "metadata": {}, "outputs": [ { @@ -1806,7 +1818,7 @@ "array([5.5, 6. , 6.5, 7. ])" ] }, - "execution_count": 45, + "execution_count": 87, "metadata": {}, "output_type": "execute_result" } @@ -1817,7 +1829,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 92, "metadata": {}, "outputs": [ { @@ -1826,13 +1838,13 @@ "array([3.5, 4. , 4.5, 5. , 5.5])" ] }, - "execution_count": 46, + "execution_count": 92, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "x[(3\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mA\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mv1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mA\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mv1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (2,3) (5,) " ] } @@ -2318,20 +2330,20 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 102, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[1.61923857, 0.95558371, 1.11141445, 1.64271612, 1.80666486],\n", - " [1.71358703, 1.45250572, 1.01957436, 1.72544194, 2.10519363],\n", - " [1.5255213 , 0.55960695, 1.43264196, 1.61597327, 1.47385403],\n", - " [0.51089051, 0.59210298, 0.45696844, 0.59261034, 0.72884524],\n", - " [0.81307747, 0.52389725, 0.65030053, 0.84393132, 0.88557227]])" + "array([[0.3767892 , 1.47079714, 0.31117826, 1.29726746, 0.51486767],\n", + " [0.25604237, 0.97247777, 0.34479677, 0.93969314, 0.3976715 ],\n", + " [0.81557228, 1.22841789, 0.86636095, 0.93499185, 0.28560187],\n", + " [0.52515694, 1.56792282, 1.1443364 , 1.84965072, 0.74141231],\n", + " [0.78004097, 1.51298694, 1.22023006, 1.42991218, 0.71648303]])" ] }, - "execution_count": 64, + "execution_count": 102, "metadata": {}, "output_type": "execute_result" } @@ -2345,16 +2357,16 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 107, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([6.30499252, 7.63515634, 4.90215304, 4.16180701, 2.98227738])" + "array([3.03824466, 2.65209134, 2.94637897, 6.50153897, 5.54270391])" ] }, - "execution_count": 66, + "execution_count": 107, "metadata": {}, "output_type": "execute_result" } @@ -2365,7 +2377,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 108, "metadata": {}, "outputs": [ { @@ -2374,7 +2386,7 @@ "30" ] }, - "execution_count": 67, + "execution_count": 108, "metadata": {}, "output_type": "execute_result" } @@ -2392,7 +2404,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 111, "metadata": {}, "outputs": [], "source": [ @@ -2402,21 +2414,20 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 112, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "matrix([[1],\n", + "matrix([[0],\n", + " [1],\n", " [2],\n", " [3],\n", - " [4],\n", - " [5],\n", - " [6]])" + " [4]])" ] }, - "execution_count": 79, + "execution_count": 112, "metadata": {}, "output_type": "execute_result" } @@ -2427,20 +2438,20 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 113, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "matrix([[1.61923857, 0.95558371, 1.11141445, 1.64271612, 1.80666486],\n", - " [1.71358703, 1.45250572, 1.01957436, 1.72544194, 2.10519363],\n", - " [1.5255213 , 0.55960695, 1.43264196, 1.61597327, 1.47385403],\n", - " [0.51089051, 0.59210298, 0.45696844, 0.59261034, 0.72884524],\n", - " [0.81307747, 0.52389725, 0.65030053, 0.84393132, 0.88557227]])" + "matrix([[0.3767892 , 1.47079714, 0.31117826, 1.29726746, 0.51486767],\n", + " [0.25604237, 0.97247777, 0.34479677, 0.93969314, 0.3976715 ],\n", + " [0.81557228, 1.22841789, 0.86636095, 0.93499185, 0.28560187],\n", + " [0.52515694, 1.56792282, 1.1443364 , 1.84965072, 0.74141231],\n", + " [0.78004097, 1.51298694, 1.22023006, 1.42991218, 0.71648303]])" ] }, - "execution_count": 70, + "execution_count": 113, "metadata": {}, "output_type": "execute_result" } @@ -2451,20 +2462,20 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 114, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "matrix([[6.30499252],\n", - " [7.63515634],\n", - " [4.90215304],\n", - " [4.16180701],\n", - " [2.98227738]])" + "matrix([[3.03824466],\n", + " [2.65209134],\n", + " [2.94637897],\n", + " [6.50153897],\n", + " [5.54270391]])" ] }, - "execution_count": 71, + "execution_count": 114, "metadata": {}, "output_type": "execute_result" } @@ -2475,7 +2486,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 117, "metadata": {}, "outputs": [ { @@ -2484,7 +2495,7 @@ "matrix([[30]])" ] }, - "execution_count": 72, + "execution_count": 117, "metadata": {}, "output_type": "execute_result" } @@ -2496,20 +2507,20 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 118, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "matrix([[ 30],\n", - " [131],\n", - " [232],\n", - " [333],\n", - " [434]])" + "matrix([[3.03824466],\n", + " [3.65209134],\n", + " [4.94637897],\n", + " [9.50153897],\n", + " [9.54270391]])" ] }, - "execution_count": 97, + "execution_count": 118, "metadata": {}, "output_type": "execute_result" } @@ -2528,7 +2539,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 125, "metadata": {}, "outputs": [], "source": [ @@ -2537,16 +2548,16 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 123, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "((5, 5), (6, 1))" + "((5, 5), (5, 1))" ] }, - "execution_count": 77, + "execution_count": 123, "metadata": {}, "output_type": "execute_result" } @@ -2557,20 +2568,22 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 124, "metadata": {}, "outputs": [ { - "ename": "ValueError", - "evalue": "shapes (5,5) and (6,1) not aligned: 5 (dim 1) != 6 (dim 0)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mM\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/.virtualenv/dl/lib/python3.5/site-packages/numpy/matrixlib/defmatrix.py\u001b[0m in \u001b[0;36m__mul__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m 307\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mN\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 308\u001b[0m \u001b[0;31m# This promotes 1-D vectors to row vectors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 309\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mN\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0masmatrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 310\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misscalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'__rmul__'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 311\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mN\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: shapes (5,5) and (6,1) not aligned: 5 (dim 1) != 6 (dim 0)" - ] + "data": { + "text/plain": [ + "matrix([[5.06458489],\n", + " [4.08471675],\n", + " [4.990684 ],\n", + " [9.17423165],\n", + " [8.08502244]])" + ] + }, + "execution_count": 124, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -2602,20 +2615,20 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 126, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[0.52324656 0.21248895 0.81560972 0.45277567]\n", - " [0.57447573 0.06345405 0.41295542 0.18753998]\n", - " [0.46850626 0.20506338 0.97890548 0.79394942]]\n", - "[[0.52324656 0.57447573 0.46850626]\n", - " [0.21248895 0.06345405 0.20506338]\n", - " [0.81560972 0.41295542 0.97890548]\n", - " [0.45277567 0.18753998 0.79394942]]\n" + "[[0.04208911 0.65828119 0.21987187 0.10069326]\n", + " [0.61960112 0.52726045 0.35884175 0.51931613]\n", + " [0.66708619 0.76886997 0.06792093 0.6548313 ]]\n", + "[[0.04208911 0.61960112 0.66708619]\n", + " [0.65828119 0.52726045 0.76886997]\n", + " [0.21987187 0.35884175 0.06792093]\n", + " [0.10069326 0.51931613 0.6548313 ]]\n" ] } ], @@ -2627,7 +2640,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 127, "metadata": {}, "outputs": [ { @@ -2637,7 +2650,7 @@ " [0.+3.j, 0.+4.j]])" ] }, - "execution_count": 83, + "execution_count": 127, "metadata": {}, "output_type": "execute_result" } @@ -2649,17 +2662,17 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 128, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "matrix([[ 0.-1.j, 0.-2.j],\n", - " [ 0.-3.j, 0.-4.j]])" + "matrix([[0.-1.j, 0.-2.j],\n", + " [0.-3.j, 0.-4.j]])" ] }, - "execution_count": 102, + "execution_count": 128, "metadata": {}, "output_type": "execute_result" } @@ -2677,17 +2690,17 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 129, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "matrix([[ 0.-1.j, 0.-3.j],\n", - " [ 0.-2.j, 0.-4.j]])" + "matrix([[0.-1.j, 0.-3.j],\n", + " [0.-2.j, 0.-4.j]])" ] }, - "execution_count": 103, + "execution_count": 129, "metadata": {}, "output_type": "execute_result" } @@ -2705,17 +2718,17 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 130, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "matrix([[ 0., 0.],\n", - " [ 0., 0.]])" + "matrix([[0., 0.],\n", + " [0., 0.]])" ] }, - "execution_count": 104, + "execution_count": 130, "metadata": {}, "output_type": "execute_result" } @@ -2726,17 +2739,17 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 131, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "matrix([[ 1., 2.],\n", - " [ 3., 4.]])" + "matrix([[1., 2.],\n", + " [3., 4.]])" ] }, - "execution_count": 105, + "execution_count": 131, "metadata": {}, "output_type": "execute_result" } @@ -2810,7 +2823,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 132, "metadata": {}, "outputs": [ { @@ -2820,7 +2833,7 @@ " [0.-1.5j, 0.+0.5j]])" ] }, - "execution_count": 85, + "execution_count": 132, "metadata": {}, "output_type": "execute_result" } @@ -2831,17 +2844,17 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 133, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "matrix([[ 1.00000000e+00+0.j, 4.44089210e-16+0.j],\n", - " [ 0.00000000e+00+0.j, 1.00000000e+00+0.j]])" + "matrix([[1.00000000e+00+0.j, 0.00000000e+00+0.j],\n", + " [2.22044605e-16+0.j, 1.00000000e+00+0.j]])" ] }, - "execution_count": 109, + "execution_count": 133, "metadata": {}, "output_type": "execute_result" } @@ -2859,7 +2872,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 134, "metadata": {}, "outputs": [ { @@ -2868,7 +2881,7 @@ "(2.0000000000000004+0j)" ] }, - "execution_count": 86, + "execution_count": 134, "metadata": {}, "output_type": "execute_result" } @@ -2879,16 +2892,16 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 135, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(0.50000000000000011+0j)" + "(0.49999999999999967+0j)" ] }, - "execution_count": 111, + "execution_count": 135, "metadata": {}, "output_type": "execute_result" } @@ -2915,7 +2928,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 136, "metadata": {}, "outputs": [ { @@ -2924,7 +2937,7 @@ "(77431, 7)" ] }, - "execution_count": 87, + "execution_count": 136, "metadata": {}, "output_type": "execute_result" } @@ -2972,9 +2985,20 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 137, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.4764047026464162" + ] + }, + "execution_count": 137, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "A = np.random.rand(4, 3)\n", "np.mean(A)" @@ -2996,7 +3020,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 138, "metadata": {}, "outputs": [ { @@ -3005,7 +3029,7 @@ "(8.282271621340573, 68.59602320966341)" ] }, - "execution_count": 89, + "execution_count": 138, "metadata": {}, "output_type": "execute_result" } @@ -3023,7 +3047,7 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 139, "metadata": {}, "outputs": [ { @@ -3032,7 +3056,7 @@ "-25.8" ] }, - "execution_count": 90, + "execution_count": 139, "metadata": {}, "output_type": "execute_result" } @@ -3044,7 +3068,7 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 140, "metadata": {}, "outputs": [ { @@ -3053,7 +3077,7 @@ "28.3" ] }, - "execution_count": 91, + "execution_count": 140, "metadata": {}, "output_type": "execute_result" } @@ -3072,7 +3096,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 141, "metadata": {}, "outputs": [ { @@ -3081,7 +3105,7 @@ "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" ] }, - "execution_count": 92, + "execution_count": 141, "metadata": {}, "output_type": "execute_result" } @@ -3093,7 +3117,7 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 142, "metadata": {}, "outputs": [ { @@ -3102,7 +3126,7 @@ "45" ] }, - "execution_count": 93, + "execution_count": 142, "metadata": {}, "output_type": "execute_result" } @@ -3114,7 +3138,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 143, "metadata": {}, "outputs": [ { @@ -3123,7 +3147,7 @@ "3628800" ] }, - "execution_count": 94, + "execution_count": 143, "metadata": {}, "output_type": "execute_result" } @@ -3135,7 +3159,7 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 144, "metadata": {}, "outputs": [ { @@ -3144,7 +3168,7 @@ "array([ 0, 1, 3, 6, 10, 15, 21, 28, 36, 45])" ] }, - "execution_count": 95, + "execution_count": 144, "metadata": {}, "output_type": "execute_result" } @@ -3156,7 +3180,7 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 147, "metadata": {}, "outputs": [ { @@ -3166,7 +3190,7 @@ " 40320, 362880, 3628800])" ] }, - "execution_count": 97, + "execution_count": 147, "metadata": {}, "output_type": "execute_result" } @@ -3178,16 +3202,16 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 148, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "1.565606093694652" + "1.04879166276667" ] }, - "execution_count": 98, + "execution_count": 148, "metadata": {}, "output_type": "execute_result" } @@ -3215,7 +3239,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 149, "metadata": {}, "outputs": [ { @@ -3263,7 +3287,7 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 150, "metadata": {}, "outputs": [ { @@ -3281,7 +3305,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 151, "metadata": {}, "outputs": [ { @@ -3308,7 +3332,7 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 153, "metadata": {}, "outputs": [ { @@ -3350,18 +3374,18 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 157, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[0.37256155, 0.55372079, 0.79188821],\n", - " [0.31015577, 0.15060291, 0.35533741],\n", - " [0.74538933, 0.09260393, 0.97473277]])" + "array([[0.99782852, 0.15992805, 0.31262638],\n", + " [0.51702607, 0.45658172, 0.66789036],\n", + " [0.77771351, 0.42574723, 0.14011317]])" ] }, - "execution_count": 2, + "execution_count": 157, "metadata": {}, "output_type": "execute_result" } @@ -3375,16 +3399,16 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 158, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.9747327654750723" + "0.997828517861979" ] }, - "execution_count": 3, + "execution_count": 158, "metadata": {}, "output_type": "execute_result" } @@ -3396,16 +3420,16 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 159, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([0.74538933, 0.55372079, 0.97473277])" + "array([0.99782852, 0.45658172, 0.66789036])" ] }, - "execution_count": 4, + "execution_count": 159, "metadata": {}, "output_type": "execute_result" } @@ -3417,16 +3441,16 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 160, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([0.79188821, 0.35533741, 0.97473277])" + "array([0.99782852, 0.66789036, 0.77771351])" ] }, - "execution_count": 5, + "execution_count": 160, "metadata": {}, "output_type": "execute_result" } @@ -3459,17 +3483,17 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 162, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[0.32932131 0.81267957 0.85936584]\n", - " [0.39382838 0.47577722 0.05040035]\n", - " [0.40055451 0.35697994 0.18257562]\n", - " [0.22687327 0.21119009 0.35634197]]\n" + "[[0.97579482 0.78668761 0.61373444]\n", + " [0.58850244 0.9784108 0.08465447]\n", + " [0.57262123 0.44795615 0.75564229]\n", + " [0.36770219 0.34095592 0.16259103]]\n" ] } ], @@ -3482,7 +3506,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 163, "metadata": {}, "outputs": [ { @@ -3500,18 +3524,18 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 166, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[0.32932131, 0.81267957, 0.85936584, 0.39382838, 0.47577722,\n", - " 0.05040035, 0.40055451, 0.35697994, 0.18257562, 0.22687327,\n", - " 0.21119009, 0.35634197]])" + "array([[0.97579482, 0.78668761, 0.61373444, 0.58850244, 0.9784108 ,\n", + " 0.08465447, 0.57262123, 0.44795615, 0.75564229, 0.36770219,\n", + " 0.34095592, 0.16259103]])" ] }, - "execution_count": 12, + "execution_count": 166, "metadata": {}, "output_type": "execute_result" } @@ -3523,25 +3547,25 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 167, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[0.32932131]\n", - " [0.81267957]\n", - " [0.85936584]\n", - " [0.39382838]\n", - " [0.47577722]\n", - " [0.05040035]\n", - " [0.40055451]\n", - " [0.35697994]\n", - " [0.18257562]\n", - " [0.22687327]\n", - " [0.21119009]\n", - " [0.35634197]]\n" + "[[0.97579482]\n", + " [0.78668761]\n", + " [0.61373444]\n", + " [0.58850244]\n", + " [0.9784108 ]\n", + " [0.08465447]\n", + " [0.57262123]\n", + " [0.44795615]\n", + " [0.75564229]\n", + " [0.36770219]\n", + " [0.34095592]\n", + " [0.16259103]]\n" ] } ], @@ -3552,18 +3576,18 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 168, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[5. , 5. , 5. , 5. , 5. ,\n", - " 0.05040035, 0.40055451, 0.35697994, 0.18257562, 0.22687327,\n", - " 0.21119009, 0.35634197]])" + " 0.08465447, 0.57262123, 0.44795615, 0.75564229, 0.36770219,\n", + " 0.34095592, 0.16259103]])" ] }, - "execution_count": 14, + "execution_count": 168, "metadata": {}, "output_type": "execute_result" } @@ -3576,19 +3600,19 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 169, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[5. , 5. , 5. ],\n", - " [5. , 5. , 0.05040035],\n", - " [0.40055451, 0.35697994, 0.18257562],\n", - " [0.22687327, 0.21119009, 0.35634197]])" + " [5. , 5. , 0.08465447],\n", + " [0.57262123, 0.44795615, 0.75564229],\n", + " [0.36770219, 0.34095592, 0.16259103]])" ] }, - "execution_count": 15, + "execution_count": 169, "metadata": {}, "output_type": "execute_result" } @@ -3606,18 +3630,18 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 170, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([5. , 5. , 5. , 5. , 5. ,\n", - " 0.05040035, 0.40055451, 0.35697994, 0.18257562, 0.22687327,\n", - " 0.21119009, 0.35634197])" + " 0.08465447, 0.57262123, 0.44795615, 0.75564229, 0.36770219,\n", + " 0.34095592, 0.16259103])" ] }, - "execution_count": 16, + "execution_count": 170, "metadata": {}, "output_type": "execute_result" } @@ -3630,7 +3654,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 171, "metadata": {}, "outputs": [ { @@ -3647,23 +3671,23 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 172, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[0.52082931 0.58821997 0.70808793 0.34761316 0.11281727 0.11376124\n", - " 0.08101192 0.94869316 0.49396522 0.9028535 0.88374347 0.15748069\n", - " 0.40813454 0.43204046 0.10210289 0.53834053 0.2108077 0.16937058\n", - " 0.2986501 0.3484833 0.94668855 0.55656648 0.79644948 0.42907108\n", - " 0.43089653 0.34901272 0.00796387 0.2622972 0.58181746 0.40590039\n", - " 0.74013682 0.2209137 0.9875646 0.1765581 0.67434898 0.46760903\n", - " 0.11710641 0.37788507 0.09466252 0.37517915 0.33241018 0.45899419\n", - " 0.27653395 0.3303985 0.44020914 0.93743914 0.5024886 0.48790875\n", - " 0.3113069 0.63876202 0.87791101 0.47762896 0.51462614 0.26859618\n", - " 0.79257729 0.12382349 0.33468532 0.00623251 0.7782686 0.41511344]\n" + "[0.0643267 0.02070895 0.01127191 0.36318507 0.26309744 0.8332378\n", + " 0.79477743 0.52745619 0.35675021 0.55907373 0.18993756 0.15919449\n", + " 0.54789401 0.23186893 0.02898541 0.43545343 0.80684175 0.44014057\n", + " 0.05129167 0.95111801 0.40743132 0.57197596 0.6692788 0.80824496\n", + " 0.40301441 0.84369196 0.95294593 0.14876807 0.58005171 0.30849079\n", + " 0.27846197 0.01062528 0.62870079 0.6416306 0.76945123 0.39443503\n", + " 0.76619764 0.42833327 0.60720341 0.16246792 0.76067082 0.27134944\n", + " 0.36268568 0.78501742 0.36935191 0.43410334 0.10594888 0.12941728\n", + " 0.51760718 0.57260509 0.09756568 0.13216908 0.32918105 0.9338644\n", + " 0.71681907 0.58218819 0.58798528 0.81665138 0.73604797 0.91730101]\n" ] } ], @@ -3675,18 +3699,18 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 176, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([10. , 10. , 10. , 10. , 10. ,\n", - " 0.05040035, 0.40055451, 0.35697994, 0.18257562, 0.22687327,\n", - " 0.21119009, 0.35634197])" + " 0.08465447, 0.57262123, 0.44795615, 0.75564229, 0.36770219,\n", + " 0.34095592, 0.16259103])" ] }, - "execution_count": 18, + "execution_count": 176, "metadata": {}, "output_type": "execute_result" } @@ -3699,19 +3723,19 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 177, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[5. , 5. , 5. ],\n", - " [5. , 5. , 0.05040035],\n", - " [0.40055451, 0.35697994, 0.18257562],\n", - " [0.22687327, 0.21119009, 0.35634197]])" + " [5. , 5. , 0.08465447],\n", + " [0.57262123, 0.44795615, 0.75564229],\n", + " [0.36770219, 0.34095592, 0.16259103]])" ] }, - "execution_count": 19, + "execution_count": 177, "metadata": {}, "output_type": "execute_result" } @@ -3736,7 +3760,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 178, "metadata": {}, "outputs": [], "source": [ @@ -3745,7 +3769,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 179, "metadata": {}, "outputs": [ { @@ -3754,7 +3778,7 @@ "(3,)" ] }, - "execution_count": 23, + "execution_count": 179, "metadata": {}, "output_type": "execute_result" } @@ -3765,7 +3789,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 180, "metadata": {}, "outputs": [ { @@ -3782,50 +3806,46 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 182, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[1]\n", - " [2]\n", - " [3]]\n" + "(3, 1)\n" ] } ], "source": [ "v2 = v.reshape(3, 1)\n", - "print(v2)" + "print(v2.shape)" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 190, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "array([[1],\n", - " [2],\n", - " [3]])" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "(3,)\n", + "(3, 1)\n" + ] } ], "source": [ "# make a column matrix of the vector v\n", - "v[:, np.newaxis]" + "v2 = v[:, np.newaxis]\n", + "print(v.shape)\n", + "print(v2.shape)\n" ] }, { "cell_type": "code", - "execution_count": 143, + "execution_count": 191, "metadata": {}, "outputs": [ { @@ -3834,7 +3854,7 @@ "(3, 1)" ] }, - "execution_count": 143, + "execution_count": 191, "metadata": {}, "output_type": "execute_result" } @@ -3888,7 +3908,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 192, "metadata": {}, "outputs": [], "source": [ @@ -3897,7 +3917,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 194, "metadata": {}, "outputs": [ { @@ -3914,7 +3934,7 @@ "array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4])" ] }, - "execution_count": 31, + "execution_count": 194, "metadata": {}, "output_type": "execute_result" } @@ -3922,14 +3942,13 @@ "source": [ "print(a)\n", "\n", - "np.repeat?\n", "# repeat each element 3 times\n", "np.repeat(a, 3)" ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 195, "metadata": {}, "outputs": [ { @@ -3939,7 +3958,7 @@ " [3, 4, 3, 4, 3, 4]])" ] }, - "execution_count": 32, + "execution_count": 195, "metadata": {}, "output_type": "execute_result" } @@ -3951,7 +3970,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 196, "metadata": {}, "outputs": [ { @@ -3961,7 +3980,7 @@ " [3, 4, 3, 4, 3, 4]])" ] }, - "execution_count": 35, + "execution_count": 196, "metadata": {}, "output_type": "execute_result" } @@ -4005,7 +4024,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 197, "metadata": {}, "outputs": [], "source": [ @@ -4014,19 +4033,20 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 198, "metadata": {}, "outputs": [ { - "ename": "ValueError", - "evalue": "all the input array dimensions except for the concatenation axis must match exactly", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mValueError\u001b[0m: all the input array dimensions except for the concatenation axis must match exactly" - ] + "data": { + "text/plain": [ + "array([[1, 2],\n", + " [3, 4],\n", + " [5, 6]])" + ] + }, + "execution_count": 198, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -4035,7 +4055,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 200, "metadata": {}, "outputs": [ { @@ -4045,7 +4065,7 @@ " [3, 4, 6]])" ] }, - "execution_count": 45, + "execution_count": 200, "metadata": {}, "output_type": "execute_result" } @@ -4063,7 +4083,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 201, "metadata": {}, "outputs": [ { @@ -4074,7 +4094,7 @@ " [5, 6]])" ] }, - "execution_count": 46, + "execution_count": 201, "metadata": {}, "output_type": "execute_result" } @@ -4085,7 +4105,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 202, "metadata": {}, "outputs": [ { @@ -4095,7 +4115,7 @@ " [3, 4, 6]])" ] }, - "execution_count": 47, + "execution_count": 202, "metadata": {}, "output_type": "execute_result" } @@ -4120,7 +4140,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 203, "metadata": {}, "outputs": [ { @@ -4130,7 +4150,7 @@ " [3, 4]])" ] }, - "execution_count": 48, + "execution_count": 203, "metadata": {}, "output_type": "execute_result" } @@ -4143,7 +4163,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 204, "metadata": {}, "outputs": [], "source": [ @@ -4153,7 +4173,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 205, "metadata": {}, "outputs": [ { @@ -4163,7 +4183,7 @@ " [ 3, 4]])" ] }, - "execution_count": 50, + "execution_count": 205, "metadata": {}, "output_type": "execute_result" } @@ -4177,7 +4197,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 206, "metadata": {}, "outputs": [ { @@ -4187,7 +4207,7 @@ " [ 3, 4]])" ] }, - "execution_count": 51, + "execution_count": 206, "metadata": {}, "output_type": "execute_result" } @@ -4205,7 +4225,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 207, "metadata": {}, "outputs": [], "source": [ @@ -4214,7 +4234,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 208, "metadata": {}, "outputs": [ { @@ -4224,7 +4244,7 @@ " [ 3, 4]])" ] }, - "execution_count": 53, + "execution_count": 208, "metadata": {}, "output_type": "execute_result" } @@ -4238,7 +4258,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 209, "metadata": {}, "outputs": [ { @@ -4248,7 +4268,7 @@ " [ 3, 4]])" ] }, - "execution_count": 54, + "execution_count": 209, "metadata": {}, "output_type": "execute_result" } @@ -4275,7 +4295,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 210, "metadata": {}, "outputs": [ { @@ -4298,7 +4318,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 211, "metadata": {}, "outputs": [ { @@ -4398,7 +4418,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 213, "metadata": {}, "outputs": [], "source": [ @@ -4414,18 +4434,19 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 214, "metadata": {}, "outputs": [ { - "ename": "NameError", - "evalue": "name 'array' is not defined", + "ename": "ValueError", + "evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mTheta\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mNameError\u001b[0m: name 'array' is not defined" + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mTheta\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36mTheta\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mScalar\u001b[0m \u001b[0mimplemenation\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mHeaviside\u001b[0m \u001b[0mstep\u001b[0m \u001b[0mfunction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \"\"\"\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()" ] } ], @@ -4444,7 +4465,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 215, "metadata": {}, "outputs": [], "source": [ @@ -4453,7 +4474,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 216, "metadata": {}, "outputs": [ { @@ -4462,7 +4483,7 @@ "array([0, 0, 0, 1, 1, 1, 1])" ] }, - "execution_count": 61, + "execution_count": 216, "metadata": {}, "output_type": "execute_result" } @@ -4480,7 +4501,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 217, "metadata": {}, "outputs": [], "source": [ @@ -4493,7 +4514,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 219, "metadata": {}, "outputs": [ { @@ -4502,7 +4523,7 @@ "array([0, 0, 0, 1, 1, 1, 1])" ] }, - "execution_count": 64, + "execution_count": 219, "metadata": {}, "output_type": "execute_result" } @@ -4513,16 +4534,23 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 221, "metadata": {}, "outputs": [ { + "name": "stdout", + "output_type": "stream", + "text": [ + "[False False False True True True True]\n" + ] + }, + { "data": { "text/plain": [ "array([0, 0, 0, 1, 1, 1, 1])" ] }, - "execution_count": 67, + "execution_count": 221, "metadata": {}, "output_type": "execute_result" } @@ -4530,12 +4558,13 @@ "source": [ "a = np.array([-3,-2,-1,0,1,2,3])\n", "b = a>=0\n", + "print(b)\n", "b*1" ] }, { "cell_type": "code", - "execution_count": 170, + "execution_count": 222, "metadata": {}, "outputs": [ { @@ -4544,7 +4573,7 @@ "(0, 1)" ] }, - "execution_count": 170, + "execution_count": 222, "metadata": {}, "output_type": "execute_result" } @@ -4570,7 +4599,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 223, "metadata": {}, "outputs": [ { @@ -4580,7 +4609,7 @@ " [3, 4]])" ] }, - "execution_count": 69, + "execution_count": 223, "metadata": {}, "output_type": "execute_result" } @@ -4592,7 +4621,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 224, "metadata": {}, "outputs": [ { @@ -4601,7 +4630,7 @@ "True" ] }, - "execution_count": 71, + "execution_count": 224, "metadata": {}, "output_type": "execute_result" } @@ -4612,7 +4641,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 225, "metadata": {}, "outputs": [ { @@ -4632,7 +4661,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 226, "metadata": {}, "outputs": [ { @@ -4666,7 +4695,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 227, "metadata": {}, "outputs": [ { @@ -4675,7 +4704,7 @@ "dtype('int64')" ] }, - "execution_count": 74, + "execution_count": 227, "metadata": {}, "output_type": "execute_result" } @@ -4686,7 +4715,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 228, "metadata": {}, "outputs": [ { @@ -4696,7 +4725,7 @@ " [3., 4.]])" ] }, - "execution_count": 75, + "execution_count": 228, "metadata": {}, "output_type": "execute_result" } @@ -4709,7 +4738,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 229, "metadata": {}, "outputs": [ { @@ -4718,7 +4747,7 @@ "dtype('float64')" ] }, - "execution_count": 76, + "execution_count": 229, "metadata": {}, "output_type": "execute_result" } @@ -4729,7 +4758,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 230, "metadata": {}, "outputs": [ { @@ -4739,7 +4768,7 @@ " [ True, True]])" ] }, - "execution_count": 77, + "execution_count": 230, "metadata": {}, "output_type": "execute_result" } diff --git a/1_numpy_matplotlib_scipy_sympy/random-matrix.csv b/1_numpy_matplotlib_scipy_sympy/random-matrix.csv new file mode 100644 index 0000000..d8d72f9 --- /dev/null +++ b/1_numpy_matplotlib_scipy_sympy/random-matrix.csv @@ -0,0 +1,3 @@ +0.85031 0.33331 0.64003 +0.52522 0.21573 0.33288 +0.74605 0.35135 0.45873 diff --git a/1_numpy_matplotlib_scipy_sympy/random-matrix.npy b/1_numpy_matplotlib_scipy_sympy/random-matrix.npy new file mode 100644 index 0000000..ef1604a Binary files /dev/null and b/1_numpy_matplotlib_scipy_sympy/random-matrix.npy differ diff --git a/1_numpy_matplotlib_scipy_sympy/scipy_tutorial.ipynb b/1_numpy_matplotlib_scipy_sympy/scipy_tutorial.ipynb index 5f0a8b0..bfbf932 100644 --- a/1_numpy_matplotlib_scipy_sympy/scipy_tutorial.ipynb +++ b/1_numpy_matplotlib_scipy_sympy/scipy_tutorial.ipynb @@ -910,37 +910,36 @@ }, { "cell_type": "code", - "execution_count": 33, - "metadata": { - "collapsed": true - }, + "execution_count": 5, + "metadata": {}, "outputs": [], "source": [ - "from scipy.linalg import *" + "from scipy.linalg import *\n", + "import numpy as np" ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ - "A = array([[1,2,3], [4,5,6], [7,8,9]])\n", - "b = array([1,2,3])" + "A = np.array([[1,2], [4,5]])\n", + "b = np.array([1,2])" ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([-0.33333333, 0.66666667, 0. ])" + "array([-0.33333333, 0.66666667])" ] }, - "execution_count": 35, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -953,23 +952,23 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([ -1.11022302e-16, 0.00000000e+00, 0.00000000e+00])" + "array([0., 0.])" ] }, - "execution_count": 36, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# check\n", - "dot(A, x) - b" + "np.dot(A, x) - b" ] }, { @@ -1067,7 +1066,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -1076,16 +1075,16 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([ 1.08466629+0.j, 0.33612878+0.j, -0.28229973+0.j])" + "array([-0.46410162+0.j, 6.46410162+0.j])" ] }, - "execution_count": 42, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } diff --git a/1_numpy_matplotlib_scipy_sympy/sympy_tutorial.ipynb b/1_numpy_matplotlib_scipy_sympy/sympy_tutorial.ipynb index 77acc51..c4de80e 100644 --- a/1_numpy_matplotlib_scipy_sympy/sympy_tutorial.ipynb +++ b/1_numpy_matplotlib_scipy_sympy/sympy_tutorial.ipynb @@ -168,7 +168,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -177,7 +177,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -186,7 +186,7 @@ "False" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -331,7 +331,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -341,7 +341,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -354,7 +354,7 @@ "4/5" ] }, - "execution_count": 11, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -365,7 +365,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -380,7 +380,7 @@ "20" ] }, - "execution_count": 12, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -433,7 +433,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -446,7 +446,7 @@ "3.1415926535897932384626433832795028841971693993751" ] }, - "execution_count": 14, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -457,7 +457,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -466,7 +466,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -480,7 +480,7 @@ "(x + 3.1416) " ] }, - "execution_count": 16, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -736,7 +736,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -749,7 +749,7 @@ "(x + 1)⋅(x + 2)⋅(x + 3)" ] }, - "execution_count": 23, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -760,7 +760,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -774,7 +774,7 @@ "x + 6⋅x + 11⋅x + 6" ] }, - "execution_count": 24, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -792,7 +792,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -805,7 +805,7 @@ "sin(a + b)" ] }, - "execution_count": 25, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -816,7 +816,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -829,7 +829,7 @@ "sin(a)⋅cos(b) + sin(b)⋅cos(a)" ] }, - "execution_count": 26, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -854,7 +854,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -867,7 +867,7 @@ "(x + 1)⋅(x + 2)⋅(x + 3)" ] }, - "execution_count": 27, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -894,7 +894,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -907,7 +907,7 @@ "(x + 1)⋅(x + 2)⋅(x + 3)" ] }, - "execution_count": 28, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -919,7 +919,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -932,7 +932,7 @@ "1" ] }, - "execution_count": 29, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -944,7 +944,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -959,7 +959,7 @@ "tan(x)" ] }, - "execution_count": 27, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -1167,7 +1167,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -1181,7 +1181,7 @@ "(x + π) " ] }, - "execution_count": 28, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -1192,7 +1192,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -1206,7 +1206,7 @@ "4⋅(x + π) " ] }, - "execution_count": 29, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -1224,7 +1224,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -1238,7 +1238,7 @@ "12⋅(x + π) " ] }, - "execution_count": 30, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -1249,12 +1249,12 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAGEAAAAbBAMAAACekfw3AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n", + "image/png": "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\n", "text/latex": [ "$$12 \\left(x + \\pi\\right)^{2}$$" ], @@ -1263,7 +1263,7 @@ "12⋅(x + π) " ] }, - "execution_count": 51, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -1281,7 +1281,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -1290,7 +1290,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -1306,7 +1306,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -1319,7 +1319,7 @@ "-x⋅(x⋅y⋅cos(x⋅y) + 2⋅sin(x⋅y))" ] }, - "execution_count": 37, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -1344,12 +1344,12 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 30, "metadata": {}, "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAKAAAAAUBAMAAAD4uit9AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nXd6gLq79C/gHf4qzfkFAyyAAAAAASUVORK5CYII=\n", + "image/png": 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"text/latex": [ "$$\\sin{\\left (x y \\right )} + \\cos{\\left (y z \\right )}$$" ], @@ -1357,7 +1357,7 @@ "sin(x⋅y) + cos(y⋅z)" ] }, - "execution_count": 55, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -1368,24 +1368,24 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$$x \\cos{\\left (y z \\right )} + \\begin{cases} 0 & \\text{for}\\: y = 0 \\\\- \\frac{1}{y} \\cos{\\left (x y \\right )} & \\text{otherwise} \\end{cases}$$" + "$$x \\cos{\\left (y z \\right )} + \\begin{cases} - \\frac{\\cos{\\left (x y \\right )}}{y} & \\text{for}\\: y \\neq 0 \\\\0 & \\text{otherwise} \\end{cases}$$" ], "text/plain": [ - " ⎛⎧ 0 for y = 0⎞\n", + " ⎛⎧-cos(x⋅y) ⎞\n", + " ⎜⎪────────── for y ≠ 0⎟\n", + "x⋅cos(y⋅z) + ⎜⎨ y ⎟\n", " ⎜⎪ ⎟\n", - "x⋅cos(y⋅z) + ⎜⎨-cos(x⋅y) ⎟\n", - " ⎜⎪────────── otherwise⎟\n", - " ⎝⎩ y ⎠" + " ⎝⎩ 0 otherwise⎠" ] }, - "execution_count": 56, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } diff --git a/2_knn/knn_classification.ipynb b/2_knn/knn_classification.ipynb index f4970c7..ec8f132 100644 --- a/2_knn/knn_classification.ipynb +++ b/2_knn/knn_classification.ipynb @@ -23,7 +23,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 算法步骤：\n", + "## 算法步骤：(FIXME)\n", "\n", "* step.1---初始化距离为最大值\n", "* step.2---计算未知样本和每个训练样本的距离dist\n", @@ -38,7 +38,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Generate sample data" + "## Generate sample data (FIXME)" ] }, { @@ -48,7 +48,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -88,7 +88,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -124,7 +124,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -177,7 +177,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -251,7 +251,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -287,7 +287,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -316,7 +316,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -345,7 +345,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ diff --git a/3_kmeans/k-means.ipynb b/3_kmeans/k-means.ipynb index 0b40cdc..de01400 100644 --- a/3_kmeans/k-means.ipynb +++ b/3_kmeans/k-means.ipynb @@ -219,7 +219,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -304,7 +304,7 @@ "4 5.0 3.6 1.4 0.2 Iris-setosa" ] }, - "execution_count": 1, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -319,6 +319,7 @@ "from numpy import *\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", + "import random\n", "\n", "# Load dataset\n", "names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']\n", @@ -328,7 +329,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 9, "metadata": { "lines_to_next_cell": 2 }, @@ -364,7 +365,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 10, "metadata": { "lines_to_next_cell": 2 }, @@ -384,7 +385,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 11, "metadata": { "lines_to_end_of_cell_marker": 2, "scrolled": true @@ -414,10 +415,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ + "import random\n", + "\n", "def randChosenCent(dataSet,k):\n", " \"\"\"初始化聚类中心：通过在区间范围随机产生的值作为新的中心点\"\"\"\n", "\n", @@ -425,15 +428,21 @@ " m=shape(dataSet)[0]\n", " # 初始化列表\n", " centroidsIndex=[]\n", + " \n", " #生成类似于样本索引的列表\n", " dataIndex=list(range(m))\n", - " for i in range(k):\n", - " #生成随机数\n", - " randIndex=random.randint(0,len(dataIndex))\n", - " #将随机产生的样本的索引放入centroidsIndex\n", - " centroidsIndex.append(dataIndex[randIndex])\n", - " #删除已经被抽中的样本\n", - " del dataIndex[randIndex]\n", + " if False:\n", + " for i in range(k):\n", + " #生成随机数\n", + " randIndex=random.randint(0,len(dataIndex))\n", + " #将随机产生的样本的索引放入centroidsIndex\n", + " centroidsIndex.append(dataIndex[randIndex])\n", + " #删除已经被抽中的样本\n", + " del dataIndex[randIndex]\n", + " else:\n", + " random.shuffle(dataIndex)\n", + " centroidsIndex = dataIndex[:k]\n", + " \n", " #根据索引获取样本\n", " centroids = dataSet.iloc[centroidsIndex]\n", " return mat(centroids)" @@ -441,7 +450,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -506,32 +515,28 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "最初的中心= [[6.4 2.7]\n", - " [5. 3.4]\n", - " [6.8 2.8]]\n", - "the SSE of 1th iteration is 52.450000\n", - "the SSE of 2th iteration is 38.174960\n", - "the SSE of 3th iteration is 38.055060\n", - "the SSE of 4th iteration is 37.980634\n", - "the SSE of 5th iteration is 37.859100\n", - "the SSE of 6th iteration is 37.783402\n", - "the SSE of 7th iteration is 37.694864\n", - "the SSE of 8th iteration is 37.636365\n", - "the SSE of 9th iteration is 37.535779\n", - "the SSE of 10th iteration is 37.454640\n", - "the SSE of 11th iteration is 37.355678\n", - "the SSE of 12th iteration is 37.290519\n", - "the SSE of 13th iteration is 37.229337\n", - "the SSE of 14th iteration is 37.201302\n", - "the SSE of 15th iteration is 37.155048\n", - "the SSE of 16th iteration is 37.141172\n" + "最初的中心= [[6.2 2.2]\n", + " [6.3 2.5]\n", + " [7.7 3.8]]\n", + "the SSE of 1th iteration is 189.420000\n", + "the SSE of 2th iteration is 70.447978\n", + "the SSE of 3th iteration is 56.041643\n", + "the SSE of 4th iteration is 49.785857\n", + "the SSE of 5th iteration is 45.985699\n", + "the SSE of 6th iteration is 43.078623\n", + "the SSE of 7th iteration is 40.594295\n", + "the SSE of 8th iteration is 37.791783\n", + "the SSE of 9th iteration is 37.235470\n", + "the SSE of 10th iteration is 37.201302\n", + "the SSE of 11th iteration is 37.155048\n", + "the SSE of 12th iteration is 37.141172\n" ] } ], @@ -543,7 +548,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -648,7 +653,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -701,7 +706,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -753,7 +758,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 29, "metadata": {}, "outputs": [ { diff --git a/6_pytorch/1_NN/deep-nn.ipynb b/6_pytorch/1_NN/deep-nn.ipynb index 5e499d7..cec052e 100644 --- a/6_pytorch/1_NN/deep-nn.ipynb +++ b/6_pytorch/1_NN/deep-nn.ipynb @@ -66,9 +66,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -83,20 +81,7 @@ "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n", - "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n", - "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n", - "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n", - "Processing...\n", - "Done!\n" - ] - } - ], + "outputs": [], "source": [ "# 使用内置函数下载 mnist 数据集\n", "train_set = mnist.MNIST('../../data/mnist', train=True, download=True)\n", @@ -113,9 +98,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "a_data, a_label = train_set[0]" @@ -128,9 +111,9 @@ "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABAElEQVR4nGNgGMyAWUhIqK5jvdSy\n/9/rGRgYGFhgEnJsVjYCwQwMDAxPJgV+vniQgYGBgREqZ7iXH8r6l/SV4dn7m8gmCt3++/fv37/H\ntn3/iMW+gDnZf/+e5WbQnoXNNXyMs/5GoQoxwVmf/n9kSGFiwAW49/11wynJoPzx4YIcRlyygR/+\n/i2XxCWru+vv32nSuGQFYv/83Y3b4p9/fzpAmSyoMnohpiwM1w5h06Q+5enfv39/bcMiJVF09+/f\nv39P+mFKiTtd/fv3799jgZiBJLT69t+/f/8eDuDEkDJf8+jv379/v7Ryo4qzMDAwMAQGMjBc3/y3\n5wM2V1IfAABFF16Aa0wAOwAAAABJRU5ErkJggg==\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABAElEQVR4nGNgGMyAWUhIqK5jvdSy/9/rGRgYGFhgEnJsVjYCwQwMDAxPJgV+vniQgYGBgREqZ7iXH8r6l/SV4dn7m8gmCt3++/fv37/Htn3/iMW+gDnZf/+e5WbQnoXNNXyMs/5GoQoxwVmf/n9kSGFiwAW49/11wynJoPzx4YIcRlyygR/+/i2XxCWru+vv32nSuGQFYv/83Y3b4p9/fzpAmSyoMnohpiwM1w5h06Q+5enfv39/bcMiJVF09+/fv39P+mFKiTtd/fv3799jgZiBJLT69t+/f/8eDuDEkDJf8+jv379/v7Ryo4qzMDAwMAQGMjBc3/y35wM2V1IfAABFF16Aa0wAOwAAAABJRU5ErkJggg==\n", "text/plain": [ - "" + "" ] }, "execution_count": 4, @@ -150,7 +133,7 @@ { "data": { "text/plain": [ - "5" + "tensor(5)" ] }, "execution_count": 5, @@ -277,7 +260,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -288,13 +271,13 @@ " x = torch.from_numpy(x)\n", " return x\n", "\n", - "train_set = mnist.MNIST('./data', train=True, transform=data_tf, download=True) # 重新载入数据集，申明定义的数据变换\n", - "test_set = mnist.MNIST('./data', train=False, transform=data_tf, download=True)" + "train_set = mnist.MNIST('../../data/mnist', train=True, transform=data_tf, download=True) # 重新载入数据集，申明定义的数据变换\n", + "test_set = mnist.MNIST('../../data/mnist', train=False, transform=data_tf, download=True)" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -302,7 +285,7 @@ "output_type": "stream", "text": [ "torch.Size([784])\n", - "5\n" + "tensor(5)\n" ] } ], @@ -314,10 +297,8 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": true - }, + "execution_count": 25, + "metadata": {}, "outputs": [], "source": [ "from torch.utils.data import DataLoader\n", @@ -335,10 +316,8 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": true - }, + "execution_count": 28, + "metadata": {}, "outputs": [], "source": [ "a, a_label = next(iter(train_data))" @@ -346,7 +325,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -366,10 +345,8 @@ }, { "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": true - }, + "execution_count": 38, + "metadata": {}, "outputs": [], "source": [ "# 使用 Sequential 定义 4 层神经网络\n", @@ -386,24 +363,24 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Sequential(\n", - " (0): Linear(in_features=784, out_features=400)\n", + " (0): Linear(in_features=784, out_features=400, bias=True)\n", " (1): ReLU()\n", - " (2): Linear(in_features=400, out_features=200)\n", + " (2): Linear(in_features=400, out_features=200, bias=True)\n", " (3): ReLU()\n", - " (4): Linear(in_features=200, out_features=100)\n", + " (4): Linear(in_features=200, out_features=100, bias=True)\n", " (5): ReLU()\n", - " (6): Linear(in_features=100, out_features=10)\n", + " (6): Linear(in_features=100, out_features=10, bias=True)\n", ")" ] }, - "execution_count": 27, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -421,10 +398,8 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": { - "collapsed": true - }, + "execution_count": 40, + "metadata": {}, "outputs": [], "source": [ "# 定义 loss 函数\n", @@ -434,35 +409,45 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 42, "metadata": { "scrolled": true }, "outputs": [ { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/bushuhui/.virtualenv/dl/lib/python3.5/site-packages/ipykernel_launcher.py:22: UserWarning: invalid index of a 0-dim tensor. This will be an error in PyTorch 0.5. Use tensor.item() to convert a 0-dim tensor to a Python number\n", + "/home/bushuhui/.virtualenv/dl/lib/python3.5/site-packages/ipykernel_launcher.py:25: UserWarning: invalid index of a 0-dim tensor. This will be an error in PyTorch 0.5. Use tensor.item() to convert a 0-dim tensor to a Python number\n", + "/home/bushuhui/.virtualenv/dl/lib/python3.5/site-packages/ipykernel_launcher.py:41: UserWarning: invalid index of a 0-dim tensor. This will be an error in PyTorch 0.5. Use tensor.item() to convert a 0-dim tensor to a Python number\n", + "/home/bushuhui/.virtualenv/dl/lib/python3.5/site-packages/ipykernel_launcher.py:44: UserWarning: invalid index of a 0-dim tensor. This will be an error in PyTorch 0.5. Use tensor.item() to convert a 0-dim tensor to a Python number\n" + ] + }, + { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 0, Train Loss: 0.525527, Train Acc: 0.830690, Eval Loss: 0.214004, Eval Acc: 0.929292\n", - "epoch: 1, Train Loss: 0.169223, Train Acc: 0.948527, Eval Loss: 0.156571, Eval Acc: 0.951048\n", - "epoch: 2, Train Loss: 0.119509, Train Acc: 0.962537, Eval Loss: 0.141246, Eval Acc: 0.955301\n", - "epoch: 3, Train Loss: 0.093633, Train Acc: 0.970349, Eval Loss: 0.096926, Eval Acc: 0.970036\n", - "epoch: 4, Train Loss: 0.077827, Train Acc: 0.975413, Eval Loss: 0.088236, Eval Acc: 0.971025\n", - "epoch: 5, Train Loss: 0.062835, Train Acc: 0.980211, Eval Loss: 0.090155, Eval Acc: 0.973200\n", - "epoch: 6, Train Loss: 0.053678, Train Acc: 0.983109, Eval Loss: 0.084136, Eval Acc: 0.974189\n", - "epoch: 7, Train Loss: 0.056607, Train Acc: 0.982343, Eval Loss: 0.075727, Eval Acc: 0.976562\n", - "epoch: 8, Train Loss: 0.040552, Train Acc: 0.986774, Eval Loss: 0.065600, Eval Acc: 0.980024\n", - "epoch: 9, Train Loss: 0.034272, Train Acc: 0.989272, Eval Loss: 0.121962, Eval Acc: 0.963212\n", - "epoch: 10, Train Loss: 0.030490, Train Acc: 0.990005, Eval Loss: 0.067141, Eval Acc: 0.979233\n", - "epoch: 11, Train Loss: 0.027200, Train Acc: 0.991188, Eval Loss: 0.160441, Eval Acc: 0.953521\n", - "epoch: 12, Train Loss: 0.023948, Train Acc: 0.991904, Eval Loss: 0.076049, Eval Acc: 0.980123\n", - "epoch: 13, Train Loss: 0.018909, Train Acc: 0.993503, Eval Loss: 0.065272, Eval Acc: 0.980518\n", - "epoch: 14, Train Loss: 0.017229, Train Acc: 0.994386, Eval Loss: 0.067790, Eval Acc: 0.981309\n", - "epoch: 15, Train Loss: 0.014564, Train Acc: 0.995253, Eval Loss: 0.067104, Eval Acc: 0.981804\n", - "epoch: 16, Train Loss: 0.013621, Train Acc: 0.995819, Eval Loss: 0.076764, Eval Acc: 0.980716\n", - "epoch: 17, Train Loss: 0.012969, Train Acc: 0.995836, Eval Loss: 0.154731, Eval Acc: 0.963805\n", - "epoch: 18, Train Loss: 0.012531, Train Acc: 0.996202, Eval Loss: 0.098053, Eval Acc: 0.975574\n", - "epoch: 19, Train Loss: 0.010139, Train Acc: 0.996635, Eval Loss: 0.072089, Eval Acc: 0.982002\n" + "epoch: 0, Train Loss: 0.166705, Train Acc: 0.947978, Eval Loss: 0.129106, Eval Acc: 0.959157\n", + "epoch: 1, Train Loss: 0.117714, Train Acc: 0.962836, Eval Loss: 0.097123, Eval Acc: 0.969838\n", + "epoch: 2, Train Loss: 0.092098, Train Acc: 0.970532, Eval Loss: 0.098194, Eval Acc: 0.969541\n", + "epoch: 3, Train Loss: 0.074442, Train Acc: 0.975880, Eval Loss: 0.077213, Eval Acc: 0.975574\n", + "epoch: 4, Train Loss: 0.062742, Train Acc: 0.979594, Eval Loss: 0.149892, Eval Acc: 0.955301\n", + "epoch: 5, Train Loss: 0.052319, Train Acc: 0.983276, Eval Loss: 0.124755, Eval Acc: 0.961531\n", + "epoch: 6, Train Loss: 0.045134, Train Acc: 0.985091, Eval Loss: 0.085263, Eval Acc: 0.975178\n", + "epoch: 7, Train Loss: 0.038610, Train Acc: 0.987423, Eval Loss: 0.063986, Eval Acc: 0.980123\n", + "epoch: 8, Train Loss: 0.033068, Train Acc: 0.988906, Eval Loss: 0.074201, Eval Acc: 0.977453\n", + "epoch: 9, Train Loss: 0.029478, Train Acc: 0.990155, Eval Loss: 0.066254, Eval Acc: 0.980123\n", + "epoch: 10, Train Loss: 0.024885, Train Acc: 0.992237, Eval Loss: 0.067818, Eval Acc: 0.979727\n", + "epoch: 11, Train Loss: 0.020706, Train Acc: 0.993237, Eval Loss: 0.174131, Eval Acc: 0.958070\n", + "epoch: 12, Train Loss: 0.019527, Train Acc: 0.993553, Eval Loss: 0.066838, Eval Acc: 0.982199\n", + "epoch: 13, Train Loss: 0.016248, Train Acc: 0.994620, Eval Loss: 0.080457, Eval Acc: 0.978738\n", + "epoch: 14, Train Loss: 0.017617, Train Acc: 0.994603, Eval Loss: 0.064320, Eval Acc: 0.982496\n", + "epoch: 15, Train Loss: 0.012970, Train Acc: 0.995985, Eval Loss: 0.079791, Eval Acc: 0.979925\n", + "epoch: 16, Train Loss: 0.012162, Train Acc: 0.995736, Eval Loss: 0.083829, Eval Acc: 0.979727\n", + "epoch: 17, Train Loss: 0.011916, Train Acc: 0.996185, Eval Loss: 0.079493, Eval Acc: 0.981507\n", + "epoch: 18, Train Loss: 0.008972, Train Acc: 0.997385, Eval Loss: 0.074135, Eval Acc: 0.981507\n", + "epoch: 19, Train Loss: 0.008857, Train Acc: 0.997018, Eval Loss: 0.074056, Eval Acc: 0.983188\n" ] } ], @@ -510,7 +495,7 @@ " eval_loss += loss.data[0]\n", " # 记录准确率\n", " _, pred = out.max(1)\n", - " num_correct = flot((pred == label).sum().data[0])\n", + " num_correct = float((pred == label).sum().data[0])\n", " acc = num_correct / im.shape[0]\n", " eval_acc += acc\n", " \n", @@ -530,7 +515,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -540,27 +525,29 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 30, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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XIiIyc2IX7hefXs/C6nI+fNcmOvt0pqqIRFPswv1FtZV87T3n0X6onw98+1GGs6PFLklE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\nYBuwAigHfg+sHNf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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -571,27 +558,29 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "Text(0.5, 1.0, 'train acc')" ] }, - "execution_count": 31, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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BS8/2A+HNbNw5IJwm+vqDrz/6Iv/842fp6Ozh/ZeezR9ccQHTp/jh8GZ2ajggnAZ+sf0A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\nM8ABwczMEg4IZmYGOCCYmVni/wORoioh1wf08AAAAABJRU5ErkJggg==\n", 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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -602,27 +591,29 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "Text(0.5, 1.0, 'test loss')" ] }, - "execution_count": 32, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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fyVblDGRVOqemvrmbeTPzpnxhPRjfOH8iDvdY4I+BtDThksUlvHigJeEndEhk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\nfvjR1fzfj9Sw70QXV33vBZ7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+ "image/png": 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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -633,27 +624,29 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "Text(0.5, 1.0, 'test acc')" ] }, - "execution_count": 33, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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jEn35w7mkNDeN/Izkt834XqlpJWsGbZgn4tvJT6eyp9bdQ05aEjlBKC01b+er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\nC/zGGBNjLPAbY0yMscBvjDEx5v8DD4zKq+qWgTkAAAAASUVORK5CYII=\n", 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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], diff --git a/6_pytorch/1_NN/nn-sequential-module.ipynb b/6_pytorch/1_NN/nn-sequential-module.ipynb index d88e8fa..9122c3c 100644 --- a/6_pytorch/1_NN/nn-sequential-module.ipynb +++ b/6_pytorch/1_NN/nn-sequential-module.ipynb @@ -103,9 +103,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "import torch\n", @@ -121,9 +119,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "def plot_decision_boundary(model, x, y):\n", @@ -180,7 +176,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 4, @@ -189,12 +185,14 @@ }, { "data": { - "image/png": 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u9Lo1Qfa6xAa7kCiEIGv1TooOpBHcIZpmI3pUG0Kxl5Tzc6+7KT2WDWf0BrVk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\n/U5yVuzAUlaJbDQjSCKiTsuAD+5DF3DyKTS0Tye3aYeWnds4VIVV5RV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NNkRJ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\nfopUyhxCuwAAAABJRU5ErkJggg==\n", 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"text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -245,11 +243,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 20, loss: 0.7033562064170837\n", - "epoch: 40, loss: 0.6739853024482727\n", - "epoch: 60, loss: 0.6731640696525574\n", - "epoch: 80, loss: 0.6731465458869934\n", - "epoch: 100, loss: 0.6731461882591248\n" + "epoch: 20, loss: 0.7048085927963257\n", + "epoch: 40, loss: 0.6740389466285706\n", + "epoch: 60, loss: 0.673165500164032\n", + "epoch: 80, loss: 0.6731466054916382\n", + "epoch: 100, loss: 0.6731460690498352\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/bushuhui/.virtualenv/dl/lib/python3.5/site-packages/ipykernel_launcher.py:8: UserWarning: invalid index of a 0-dim tensor. This will be an error in PyTorch 0.5. Use tensor.item() to convert a 0-dim tensor to a Python number\n", + " \n" ] } ], @@ -267,9 +273,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "def plot_logistic(x):\n", @@ -285,9 +289,17 @@ "metadata": {}, "outputs": [ { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/bushuhui/.virtualenv/dl/lib/python3.5/site-packages/torch/nn/functional.py:1006: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n", + " warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n" + ] + }, + { "data": { "text/plain": [ - "Text(0.5,1,'logistic regression')" + "Text(0.5, 1.0, 'logistic regression')" ] }, "execution_count": 9, @@ -296,12 +308,14 @@ }, { "data": { - "image/png": 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wxQSdEj839DwT/lZSZoR4eZb2YuKWO+53yvTmJhgKdaAWejIs8+KtFlszarZn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\nyXPoGnzpPSHXZNI0W6J5ModUCuYoXZj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dqmyK0AiPJRlZXY9tGLz+pS8QHh8nPBrF1oyy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\nAABJRU5ErkJggg==\n", 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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -319,10 +333,8 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": true - }, + "execution_count": 11, + "metadata": {}, "outputs": [], "source": [ "# 定义两层神经网络的参数\n", @@ -346,23 +358,33 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/bushuhui/.virtualenv/dl/lib/python3.5/site-packages/torch/nn/functional.py:995: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.\n", + " warnings.warn(\"nn.functional.tanh is deprecated. Use torch.tanh instead.\")\n", + "/home/bushuhui/.virtualenv/dl/lib/python3.5/site-packages/ipykernel_launcher.py:9: UserWarning: invalid index of a 0-dim tensor. This will be an error in PyTorch 0.5. Use tensor.item() to convert a 0-dim tensor to a Python number\n", + " if __name__ == '__main__':\n" + ] + }, + { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1000, loss: 0.29002276062965393\n", - "epoch: 2000, loss: 0.276983380317688\n", - "epoch: 3000, loss: 0.26818233728408813\n", - "epoch: 4000, loss: 0.2620616555213928\n", - "epoch: 5000, loss: 0.2571246325969696\n", - "epoch: 6000, loss: 0.23155273497104645\n", - "epoch: 7000, loss: 0.2241673469543457\n", - "epoch: 8000, loss: 0.220903217792511\n", - "epoch: 9000, loss: 0.21872615814208984\n", - "epoch: 10000, loss: 0.2170446664094925\n" + "epoch: 1000, loss: 0.28478434681892395\n", + "epoch: 2000, loss: 0.2721796929836273\n", + "epoch: 3000, loss: 0.26508721709251404\n", + "epoch: 4000, loss: 0.26026514172554016\n", + "epoch: 5000, loss: 0.2568226456642151\n", + "epoch: 6000, loss: 0.2542745769023895\n", + "epoch: 7000, loss: 0.25232821702957153\n", + "epoch: 8000, loss: 0.2508011758327484\n", + "epoch: 9000, loss: 0.2495756596326828\n", + "epoch: 10000, loss: 0.24857309460639954\n" ] } ], @@ -380,10 +402,8 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": true - }, + "execution_count": 13, + "metadata": {}, "outputs": [], "source": [ "def plot_network(x):\n", @@ -398,27 +418,39 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/bushuhui/.virtualenv/dl/lib/python3.5/site-packages/torch/nn/functional.py:995: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.\n", + " warnings.warn(\"nn.functional.tanh is deprecated. Use torch.tanh instead.\")\n", + "/home/bushuhui/.virtualenv/dl/lib/python3.5/site-packages/torch/nn/functional.py:1006: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n", + " warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n" + ] + }, + { "data": { "text/plain": [ - "Text(0.5,1,'2 layer network')" + "Text(0.5, 1.0, '2 layer network')" ] }, - "execution_count": 13, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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s3YOoqVOveR1iAL5QFfMuiaTetdrPN0tJ3zr3Jq7f/yheM8mtHk/x+qNP4Bom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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -462,10 +494,8 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": true - }, + "execution_count": 15, + "metadata": {}, "outputs": [], "source": [ "# Sequential\n", @@ -478,16 +508,16 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Linear(in_features=2, out_features=4)" + "Linear(in_features=2, out_features=4, bias=True)" ] }, - "execution_count": 15, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -500,7 +530,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -508,12 +538,10 @@ "output_type": "stream", "text": [ "Parameter containing:\n", - "-0.4964 0.3581\n", - "-0.0705 0.4262\n", - " 0.0601 0.1988\n", - " 0.6683 -0.4470\n", - "[torch.FloatTensor of size 4x2]\n", - "\n" + "tensor([[-0.6538, 0.6585],\n", + " [ 0.3440, 0.4386],\n", + " [ 0.1757, 0.2476],\n", + " [-0.1409, -0.2638]], requires_grad=True)\n" ] } ], @@ -526,7 +554,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -539,23 +567,31 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 21, "metadata": {}, "outputs": [ { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/bushuhui/.virtualenv/dl/lib/python3.5/site-packages/ipykernel_launcher.py:9: UserWarning: invalid index of a 0-dim tensor. This will be an error in PyTorch 0.5. Use tensor.item() to convert a 0-dim tensor to a Python number\n", + " if __name__ == '__main__':\n" + ] + }, + { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1000, loss: 0.2839296758174896\n", - "epoch: 2000, loss: 0.2716798782348633\n", - "epoch: 3000, loss: 0.2647360861301422\n", - "epoch: 4000, loss: 0.26001378893852234\n", - "epoch: 5000, loss: 0.2566395103931427\n", - "epoch: 6000, loss: 0.2541380524635315\n", - "epoch: 7000, loss: 0.25222381949424744\n", - "epoch: 8000, loss: 0.2507193386554718\n", - "epoch: 9000, loss: 0.24951006472110748\n", - "epoch: 10000, loss: 0.2485194206237793\n" + "epoch: 1000, loss: 0.28410840034484863\n", + "epoch: 2000, loss: 0.2719648480415344\n", + "epoch: 3000, loss: 0.2649618983268738\n", + "epoch: 4000, loss: 0.2594653367996216\n", + "epoch: 5000, loss: 0.23266130685806274\n", + "epoch: 6000, loss: 0.2252696454524994\n", + "epoch: 7000, loss: 0.2217651605606079\n", + "epoch: 8000, loss: 0.2194037288427353\n", + "epoch: 9000, loss: 0.2175876647233963\n", + "epoch: 10000, loss: 0.2160961925983429\n" ] } ], @@ -580,10 +616,8 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": true - }, + "execution_count": 22, + "metadata": {}, "outputs": [], "source": [ "def plot_seq(x):\n", @@ -594,27 +628,37 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 23, "metadata": {}, "outputs": [ { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/bushuhui/.virtualenv/dl/lib/python3.5/site-packages/torch/nn/functional.py:1006: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n", + " warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n" + ] + }, + { "data": { "text/plain": [ - "" + "Text(0.5, 1.0, 'sequential')" ] }, - "execution_count": 20, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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jRjIM4cKlCOtrHsW8jxhBeYtqxWd+ZtOeVCooyqU6F69Ejixsc2vIaOexbnGP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+ "image/png": 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -632,10 +676,8 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": true - }, + "execution_count": 24, + "metadata": {}, "outputs": [], "source": [ "# 将参数和模型保存在一起\n", @@ -651,10 +693,8 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": true - }, + "execution_count": 25, + "metadata": {}, "outputs": [], "source": [ "# 读取保存的模型\n", @@ -663,20 +703,20 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Sequential(\n", - " (0): Linear(in_features=2, out_features=4)\n", + " (0): Linear(in_features=2, out_features=4, bias=True)\n", " (1): Tanh()\n", - " (2): Linear(in_features=4, out_features=1)\n", + " (2): Linear(in_features=4, out_features=1, bias=True)\n", ")" ] }, - "execution_count": 23, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -687,7 +727,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -695,12 +735,10 @@ "output_type": "stream", "text": [ "Parameter containing:\n", - " -0.5532 -1.9916\n", - " 0.0446 7.9446\n", - " 10.3188 -12.9290\n", - " 10.0688 11.7754\n", - "[torch.FloatTensor of size 4x2]\n", - "\n" + "tensor([[-8.8738, 9.7847],\n", + " [10.4652, 12.2881],\n", + " [-9.4986, 2.9617],\n", + " [ 0.1037, -9.5129]], requires_grad=True)\n" ] } ], diff --git a/6_pytorch/1_NN/optimizer/adagrad.ipynb b/6_pytorch/1_NN/optimizer/adagrad.ipynb index 0f22510..bd08754 100644 --- a/6_pytorch/1_NN/optimizer/adagrad.ipynb +++ b/6_pytorch/1_NN/optimizer/adagrad.ipynb @@ -15,6 +15,8 @@ "$$\n", "\n", "这里的 $\\epsilon$ 是为了数值稳定性而加上的，因为有可能 s 的值为 0，那么 0 出现在分母就会出现无穷大的情况，通常 $\\epsilon$ 取 $10^{-10}$，这样不同的参数由于梯度不同，他们对应的 s 大小也就不同，所以上面的公式得到的学习率也就不同，这也就实现了自适应的学习率。\n", + "\n", + "FIXME: need improve. ref: https://ruder.io/optimizing-gradient-descent/index.html#adagrad\n", "\n" ] }, @@ -32,9 +34,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "def sgd_adagrad(parameters, sqrs, lr):\n", @@ -48,9 +48,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -70,8 +68,8 @@ " x = torch.from_numpy(x)\n", " return x\n", "\n", - "train_set = MNIST('./data', train=True, transform=data_tf, download=True) # 载入数据集，申明定义的数据变换\n", - "test_set = MNIST('./data', train=False, transform=data_tf, download=True)\n", + "train_set = MNIST('../../../data/mnist', train=True, transform=data_tf, download=True) # 载入数据集，申明定义的数据变换\n", + "test_set = MNIST('../../../data/mnist', train=False, transform=data_tf, download=True)\n", "\n", "# 定义 loss 函数\n", "criterion = nn.CrossEntropyLoss()" @@ -83,15 +81,23 @@ "metadata": {}, "outputs": [ { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/bushuhui/.virtualenv/dl/lib/python3.5/site-packages/ipykernel_launcher.py:31: UserWarning: invalid index of a 0-dim tensor. This will be an error in PyTorch 0.5. Use tensor.item() to convert a 0-dim tensor to a Python number\n", + "/home/bushuhui/.virtualenv/dl/lib/python3.5/site-packages/ipykernel_launcher.py:33: UserWarning: invalid index of a 0-dim tensor. This will be an error in PyTorch 0.5. Use tensor.item() to convert a 0-dim tensor to a Python number\n" + ] + }, + { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 0, Train Loss: 0.406752\n", - "epoch: 1, Train Loss: 0.248588\n", - "epoch: 2, Train Loss: 0.211789\n", - "epoch: 3, Train Loss: 0.188928\n", - "epoch: 4, Train Loss: 0.172839\n", - "使用时间: 54.70610 s\n" + "epoch: 0, Train Loss: 0.404529\n", + "epoch: 1, Train Loss: 0.243532\n", + "epoch: 2, Train Loss: 0.201834\n", + "epoch: 3, Train Loss: 0.176955\n", + "epoch: 4, Train Loss: 0.159980\n", + "使用时间: 31.24649 s\n" ] } ], diff --git a/6_pytorch/1_NN/param_initialize.ipynb b/6_pytorch/1_NN/param_initialize.ipynb index 81282e9..02f7abb 100644 --- a/6_pytorch/1_NN/param_initialize.ipynb +++ b/6_pytorch/1_NN/param_initialize.ipynb @@ -26,9 +26,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -39,9 +37,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# 定义一个 Sequential 模型\n", @@ -57,9 +53,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# 访问第一层的参数\n", @@ -77,15 +71,14 @@ "output_type": "stream", "text": [ "Parameter containing:\n", - " 0.1236 -0.1731 -0.0479 ... 0.0031 0.0784 0.1239\n", - " 0.0713 0.1615 0.0500 ... -0.1757 -0.1274 -0.1625\n", - " 0.0638 -0.1543 -0.0362 ... 0.0316 -0.1774 -0.1242\n", - " ... ⋱ ... \n", - " 0.1551 0.1772 0.1537 ... 0.0730 0.0950 0.0627\n", - " 0.0495 0.0896 0.0243 ... -0.1302 -0.0256 -0.0326\n", - "-0.1193 -0.0989 -0.1795 ... 0.0939 0.0774 -0.0751\n", - "[torch.FloatTensor of size 40x30]\n", - "\n" + "tensor([[ 0.0276, -0.1197, -0.0397, ..., 0.0759, -0.1630, 0.1599],\n", + " [ 0.1419, 0.0903, -0.1630, ..., -0.0615, 0.1502, 0.0596],\n", + " [-0.0451, 0.1103, 0.1070, ..., -0.1506, -0.1346, 0.1284],\n", + " ...,\n", + " [-0.0975, -0.1264, 0.0738, ..., -0.1058, -0.1396, 0.1800],\n", + " [-0.1352, 0.0287, 0.0779, ..., 0.1773, -0.1585, 0.1046],\n", + " [-0.1194, 0.1526, -0.0018, ..., 0.0946, -0.1453, -0.1512]],\n", + " requires_grad=True)\n" ] } ], @@ -103,9 +96,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# 定义一个 Tensor 直接对其进行替换\n", @@ -122,15 +113,14 @@ "output_type": "stream", "text": [ "Parameter containing:\n", - " 4.5768 3.6175 3.3098 ... 4.7374 4.0164 3.3037\n", - " 4.1809 3.5624 3.1452 ... 3.0305 4.4444 4.1058\n", - " 3.5277 4.3712 3.7859 ... 3.5760 4.8559 4.3252\n", - " ... ⋱ ... \n", - " 4.8983 3.9855 3.2842 ... 4.7683 4.7590 3.3498\n", - " 4.9168 4.5723 3.5870 ... 3.2032 3.9842 3.2484\n", - " 4.2532 4.6352 4.4857 ... 3.7543 3.9885 4.4211\n", - "[torch.DoubleTensor of size 40x30]\n", - "\n" + "tensor([[3.0403, 4.7550, 4.9311, ..., 3.0626, 4.3593, 3.9823],\n", + " [4.4812, 4.5463, 4.4052, ..., 3.7669, 3.4201, 4.6582],\n", + " [3.7711, 3.3997, 4.1416, ..., 3.4086, 3.1681, 4.0410],\n", + " ...,\n", + " [4.4137, 4.1779, 4.8741, ..., 3.4678, 3.4457, 4.7489],\n", + " [3.8246, 4.2699, 4.9944, ..., 4.8576, 3.8945, 4.5525],\n", + " [3.4959, 3.6991, 4.4047, ..., 4.7308, 3.5796, 3.2013]],\n", + " dtype=torch.float64, requires_grad=True)\n" ] } ], @@ -344,10 +334,8 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": true - }, + "execution_count": 7, + "metadata": {}, "outputs": [], "source": [ "from torch.nn import init" @@ -355,7 +343,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -363,15 +351,14 @@ "output_type": "stream", "text": [ "Parameter containing:\n", - " 0.8453 0.2891 -0.5276 ... -0.1530 -0.4474 -0.5470\n", - "-0.1983 -0.4530 -0.1950 ... 0.4107 -0.4889 0.3654\n", - " 0.9149 -0.5641 -0.6594 ... 0.0734 0.1354 -0.4152\n", - " ... ⋱ ... \n", - "-0.4718 -0.5125 -0.5572 ... 0.0824 -0.6551 0.0840\n", - "-0.2374 -0.0036 0.6497 ... 0.7856 -0.1367 -0.8795\n", - " 0.0774 0.2609 -0.2358 ... -0.8196 0.1696 0.5976\n", - "[torch.DoubleTensor of size 40x30]\n", - "\n" + "tensor([[3.0403, 4.7550, 4.9311, ..., 3.0626, 4.3593, 3.9823],\n", + " [4.4812, 4.5463, 4.4052, ..., 3.7669, 3.4201, 4.6582],\n", + " [3.7711, 3.3997, 4.1416, ..., 3.4086, 3.1681, 4.0410],\n", + " ...,\n", + " [4.4137, 4.1779, 4.8741, ..., 3.4678, 3.4457, 4.7489],\n", + " [3.8246, 4.2699, 4.9944, ..., 4.8576, 3.8945, 4.5525],\n", + " [3.4959, 3.6991, 4.4047, ..., 4.7308, 3.5796, 3.2013]],\n", + " dtype=torch.float64, requires_grad=True)\n" ] } ], @@ -381,24 +368,32 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 9, "metadata": {}, "outputs": [ { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/bushuhui/.virtualenv/dl/lib/python3.5/site-packages/ipykernel_launcher.py:1: UserWarning: nn.init.xavier_uniform is now deprecated in favor of nn.init.xavier_uniform_.\n", + " \"\"\"Entry point for launching an IPython kernel.\n" + ] + }, + { "data": { "text/plain": [ "Parameter containing:\n", - "-0.2114 0.2704 -0.2186 ... 0.1727 0.2158 0.0775\n", - "-0.0736 -0.0565 0.0844 ... 0.1793 0.2520 -0.0047\n", - " 0.1331 -0.1843 0.2426 ... -0.2199 -0.0689 0.1756\n", - " ... ⋱ ... \n", - " 0.2751 -0.1404 0.1225 ... 0.1926 0.0175 -0.2099\n", - " 0.0970 -0.0733 -0.2461 ... 0.0605 0.1915 -0.1220\n", - " 0.0199 0.1283 -0.1384 ... -0.0344 -0.0560 0.2285\n", - "[torch.DoubleTensor of size 40x30]" + "tensor([[-0.0889, 0.2279, 0.1816, ..., 0.1091, 0.0207, -0.2063],\n", + " [ 0.0394, 0.1860, 0.1261, ..., 0.2250, -0.2881, 0.0727],\n", + " [-0.2252, -0.0639, 0.2077, ..., 0.0328, -0.0075, 0.0339],\n", + " ...,\n", + " [-0.0932, 0.2806, -0.2377, ..., -0.2087, 0.0325, 0.0504],\n", + " [-0.2305, 0.2866, -0.1872, ..., 0.2127, 0.1487, 0.0645],\n", + " [-0.0072, 0.2771, 0.0928, ..., -0.0234, -0.1238, 0.1197]],\n", + " dtype=torch.float64, requires_grad=True)" ] }, - "execution_count": 15, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -409,7 +404,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -417,15 +412,14 @@ "output_type": "stream", "text": [ "Parameter containing:\n", - "-0.2114 0.2704 -0.2186 ... 0.1727 0.2158 0.0775\n", - "-0.0736 -0.0565 0.0844 ... 0.1793 0.2520 -0.0047\n", - " 0.1331 -0.1843 0.2426 ... -0.2199 -0.0689 0.1756\n", - " ... ⋱ ... \n", - " 0.2751 -0.1404 0.1225 ... 0.1926 0.0175 -0.2099\n", - " 0.0970 -0.0733 -0.2461 ... 0.0605 0.1915 -0.1220\n", - " 0.0199 0.1283 -0.1384 ... -0.0344 -0.0560 0.2285\n", - "[torch.DoubleTensor of size 40x30]\n", - "\n" + "tensor([[-0.0889, 0.2279, 0.1816, ..., 0.1091, 0.0207, -0.2063],\n", + " [ 0.0394, 0.1860, 0.1261, ..., 0.2250, -0.2881, 0.0727],\n", + " [-0.2252, -0.0639, 0.2077, ..., 0.0328, -0.0075, 0.0339],\n", + " ...,\n", + " [-0.0932, 0.2806, -0.2377, ..., -0.2087, 0.0325, 0.0504],\n", + " [-0.2305, 0.2866, -0.1872, ..., 0.2127, 0.1487, 0.0645],\n", + " [-0.0072, 0.2771, 0.0928, ..., -0.0234, -0.1238, 0.1197]],\n", + " dtype=torch.float64, requires_grad=True)\n" ] } ], diff --git a/6_pytorch/2_CNN/CNN_Introduction.pptx b/6_pytorch/2_CNN/CNN_Introduction.pptx index 1304b1a..56767e0 100644 Binary files a/6_pytorch/2_CNN/CNN_Introduction.pptx and b/6_pytorch/2_CNN/CNN_Introduction.pptx differ diff --git a/6_pytorch/2_CNN/lr-decay.ipynb b/6_pytorch/2_CNN/lr-decay.ipynb index 6f95b06..7bb2c5d 100644 --- a/6_pytorch/2_CNN/lr-decay.ipynb +++ b/6_pytorch/2_CNN/lr-decay.ipynb @@ -5,7 +5,7 @@ "metadata": {}, "source": [ "# 学习率衰减\n", - "对于基于一阶梯度进行优化的方法而言，开始的时候更新的幅度是比较大的，也就是说开始的学习率可以设置大一点，但是当训练集的 loss 下降到一定程度之后，，使用这个太大的学习率就会导致 loss 一直来回震荡，比如\n", + "对于基于一阶梯度进行优化的方法而言，开始的时候更新的幅度是比较大的，也就是说开始的学习率可以设置大一点，但是当训练集的 loss 下降到一定程度之后，继续使用这个太大的学习率就会导致 loss 一直来回震荡，比如\n", "\n", "![](https://ws4.sinaimg.cn/large/006tNc79ly1fmrvdlncomj30bf0aywet.jpg)" ] @@ -265,9 +265,12 @@ "if torch.cuda.is_available():\n", " net = net.cuda()\n", "prev_time = datetime.now()\n", - "for epoch in range(30):\n", + "for epoch in range(100):\n", " if epoch == 20:\n", - " set_learning_rate(optimizer, 0.01) # 80 次修改学习率为 0.01\n", + " set_learning_rate(optimizer, 0.01) # 20 次修改学习率为 0.01\n", + " elif epoch == 60:\n", + " set_learning_rate(optimizer, 0.005) # 60 次修改学习率为 0.01\n", + "\n", " train_loss = 0\n", " net = net.train()\n", " for im, label in train_data:\n", diff --git a/6_pytorch/2_CNN/resnet.ipynb b/6_pytorch/2_CNN/resnet.ipynb index 60bf725..e389a17 100644 --- a/6_pytorch/2_CNN/resnet.ipynb +++ b/6_pytorch/2_CNN/resnet.ipynb @@ -31,13 +31,12 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2017-12-22T12:56:06.772059Z", "start_time": "2017-12-22T12:56:06.766027Z" - }, - "collapsed": true + } }, "outputs": [], "source": [ @@ -54,13 +53,12 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2017-12-22T12:47:49.222432Z", "start_time": "2017-12-22T12:47:49.217940Z" - }, - "collapsed": true + } }, "outputs": [], "source": [ @@ -70,13 +68,12 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2017-12-22T13:14:02.429145Z", "start_time": "2017-12-22T13:14:02.383322Z" - }, - "collapsed": true + } }, "outputs": [], "source": [ @@ -114,7 +111,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2017-12-22T13:14:05.793185Z", @@ -142,7 +139,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2017-12-22T13:14:11.929120Z", @@ -177,13 +174,12 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2017-12-22T13:27:46.099404Z", "start_time": "2017-12-22T13:27:45.986235Z" - }, - "collapsed": true + } }, "outputs": [], "source": [ @@ -248,7 +244,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2017-12-22T13:28:00.597030Z", diff --git a/6_pytorch/2_CNN/vgg.ipynb b/6_pytorch/2_CNN/vgg.ipynb index c0be03f..3649f57 100644 --- a/6_pytorch/2_CNN/vgg.ipynb +++ b/6_pytorch/2_CNN/vgg.ipynb @@ -35,13 +35,12 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2017-12-22T09:01:51.296457Z", "start_time": "2017-12-22T09:01:50.883050Z" - }, - "collapsed": true + } }, "outputs": [], "source": [ @@ -64,19 +63,18 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2017-12-22T09:01:51.312500Z", "start_time": "2017-12-22T09:01:51.298777Z" - }, - "collapsed": true + } }, "outputs": [], "source": [ "def vgg_block(num_convs, in_channels, out_channels):\n", " net = [nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1), nn.ReLU(True)] # 定义第一层\n", - " \n", + "\n", " for i in range(num_convs-1): # 定义后面的很多层\n", " net.append(nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1))\n", " net.append(nn.ReLU(True))\n", @@ -94,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2017-12-22T08:20:40.819497Z", @@ -107,13 +105,13 @@ "output_type": "stream", "text": [ "Sequential(\n", - " (0): Conv2d (64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", " (1): ReLU(inplace)\n", - " (2): Conv2d (128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", " (3): ReLU(inplace)\n", - " (4): Conv2d (128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (4): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", " (5): ReLU(inplace)\n", - " (6): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), dilation=(1, 1))\n", + " (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", ")\n" ] } @@ -125,7 +123,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2017-12-22T07:52:04.632406Z", @@ -159,13 +157,12 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2017-12-22T09:01:54.497712Z", "start_time": "2017-12-22T09:01:54.489255Z" - }, - "collapsed": true + } }, "outputs": [], "source": [ @@ -187,7 +184,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2017-12-22T09:01:55.149378Z", @@ -201,42 +198,52 @@ "text": [ "Sequential(\n", " (0): Sequential(\n", - " (0): Conv2d (3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", " (1): ReLU(inplace)\n", - " (2): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), dilation=(1, 1))\n", + " (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (3): ReLU(inplace)\n", + " (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", " )\n", " (1): Sequential(\n", - " (0): Conv2d (64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", " (1): ReLU(inplace)\n", - " (2): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), dilation=(1, 1))\n", + " (2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (3): ReLU(inplace)\n", + " (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", " )\n", " (2): Sequential(\n", - " (0): Conv2d (128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", " (1): ReLU(inplace)\n", - " (2): Conv2d (256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", " (3): ReLU(inplace)\n", - " (4): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), dilation=(1, 1))\n", + " (4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (5): ReLU(inplace)\n", + " (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", " )\n", " (3): Sequential(\n", - " (0): Conv2d (256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (0): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", " (1): ReLU(inplace)\n", - " (2): Conv2d (512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", " (3): ReLU(inplace)\n", - " (4): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), dilation=(1, 1))\n", + " (4): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (5): ReLU(inplace)\n", + " (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", " )\n", " (4): Sequential(\n", - " (0): Conv2d (512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", " (1): ReLU(inplace)\n", - " (2): Conv2d (512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", " (3): ReLU(inplace)\n", - " (4): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), dilation=(1, 1))\n", + " (4): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (5): ReLU(inplace)\n", + " (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", " )\n", ")\n" ] } ], "source": [ - "vgg_net = vgg_stack((1, 1, 2, 2, 2), ((3, 64), (64, 128), (128, 256), (256, 512), (512, 512)))\n", + "vgg_net = vgg_stack((2, 2, 3, 3, 3), ((3, 64), (64, 128), (128, 256), (256, 512), (512, 512)))\n", "print(vgg_net)" ] }, @@ -249,7 +256,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2017-12-22T08:52:44.049650Z", @@ -280,13 +287,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2017-12-22T09:01:57.323034Z", "start_time": "2017-12-22T09:01:57.306864Z" - }, - "collapsed": true + } }, "outputs": [], "source": [ diff --git a/demo_code/3_CNN_VGG16.py b/demo_code/3_CNN_VGG16.py new file mode 100644 index 0000000..b44a216 --- /dev/null +++ b/demo_code/3_CNN_VGG16.py @@ -0,0 +1,48 @@ + +import sys +sys.path.append('..') + +import numpy as np +import torch +from torch import nn +from torch.autograd import Variable +from torchvision.datasets import CIFAR10 + + +def vgg_block(num_convs, in_channels, out_channels): + net = [nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1), \ + nn.ReLU(True)] # 定义第一层 + + for i in range(num_convs-1): # 定义后面的很多层 + net.append(nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1)) + net.append(nn.ReLU(True)) + + net.append(nn.MaxPool2d(2, 2)) # 定义池化层 + return nn.Sequential(*net) + +def vgg_stack(num_convs, channels): + net = [] + for n, c in zip(num_convs, channels): + in_c = c[0] + out_c = c[1] + net.append(vgg_block(n, in_c, out_c)) + return nn.Sequential(*net) + +vgg_net = vgg_stack((2, 2, 3, 3, 3), \ + ((3, 64), (64, 128), (128, 256), (256, 512), (512, 512))) + + +class vgg(nn.Module): + def __init__(self): + super(vgg, self).__init__() + self.feature = vgg_net + self.fc = nn.Sequential( + nn.Linear(512, 100), + nn.ReLU(True), + nn.Linear(100, 10) + ) + def forward(self, x): + x = self.feature(x) + x = x.view(x.shape[0], -1) + x = self.fc(x) + return x diff --git a/tips/构建深度神经网络的一些实战建议.md b/tips/构建深度神经网络的一些实战建议.md index 431f22c..58a025f 100644 --- a/tips/构建深度神经网络的一些实战建议.md +++ b/tips/构建深度神经网络的一些实战建议.md @@ -8,7 +8,7 @@ 更多详细的深度学习技巧等可以参考： * [Machine Learning Yearning 中文版 - 《机器学习训练秘籍》](https://github.com/deeplearning-ai/machine-learning-yearning-cn) ([在线阅读](https://deeplearning-ai.github.io/machine-learning-yearning-cn/)) - +* [33个神经网络「炼丹」技巧](https://www.toutiao.com/a6761273383452672524) ## 常见的一些tips