Improve some English representation

3_kmeans/1-k-means.ipynb

@@ -955,7 +955,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.6.9"
   }
  },
  "nbformat": 4,

3_kmeans/1-k-means_EN.ipynb

@@ -25,7 +25,7 @@
     "J = \\sum_{k=1}^{K} \\sum_{i \\in C_k} | x_i - u_k|^2\n",
     "$$\n",
     "\n",
-    "$u_k$is the centriod poisition$k$个类的重心位置，定义为：\n",
+    "$u_k$is the centriod poisition of samples from type $C_k$ with the definition of：\n",
     "$$\n",
     "u_k = \\frac{1}{|C_k|} \\sum_{x \\in C_k} x\n",
     "$$\n",
@@ -468,7 +468,7 @@
     "\n",
     "    # step1: Initialize cluster center by the sample point that generate randomly\n",
     "    centroids = randChosenCent(dataSet, k)\n",
-    "    print('最初的中心=', centroids)\n",
+    "    print('Original centers=', centroids)\n",
     "\n",
     "    # Flag bit，if the result of sample classification before and after iteration has changed, the value is True\n",
     "    clusterChanged = True\n",
@@ -483,7 +483,7 @@
     "        for i in range(m):\n",
     "            # Initially define distance as infinite\n",
     "            minDist = inf;\n",
-    "            # Initialize index value初始化索引值\n",
+    "            # Initialize index value\n",
     "            minIndex = -1\n",
     "            # Calculate the distance of each sample and k centriods\n",
     "            for j in range(k):\n",
@@ -491,7 +491,7 @@
     "                distJI = distEclud(centroids[j, :], dataSet.values[i, :])\n",
     "                # Judeg if the distance if the minimum\n",
     "                if distJI < minDist:\n",
-    "                    # Update to get the minimum distance更新获取到最小距离\n",
+    "                    # Update to get the minimum distance\n",
     "                    minDist = distJI\n",
     "                    # Get corresponding cluster numbers\n",
     "                    minIndex = j\n",
@@ -808,8 +808,7 @@
     "1. For the ith smapel in the clusterded data$x_i$, calculate the average value between $x_i$ and all the other smaple in the same cluster, written as $a_i$, used to quantify the cohesion within a cluster\n",
     "2. Choose a cluster $b$ outside of $x_i$, calculate the average distance between $x_i$ and all samples in cluster $b$, traverse all other cluster, find the closest average distance and noted as $b_i$, which can be used to quantify the degree of separation between clusters.\n",
     "3. For sample $x_i$, Silhouette Coefficient is $sc_i = \\frac{b_i−a_i}{max(b_i,a_i)}$ \n",
-    "4. Finally, calculate average value for all sample $\\mathbf{X}$, which will be the Silhouette Coefficient for current cluster result.\n",
-    "4. 最后，对所以样本集合$\\mathbf{X}$求出平均值，即为当前聚类结果的整体轮廓系数。"
+    "4. Finally, calculate average value for all sample $\\mathbf{X}$, which will be the Silhouette Coefficient for current cluster result."
    ]
   },
   {
@@ -1004,7 +1003,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.6.9"
   }
  },
  "nbformat": 4,

4_logistic_regression/1-Least_squares.ipynb

@@ -5146,7 +5146,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.6.9"
   }
  },
  "nbformat": 4,

4_logistic_regression/1-Least_squares_EN.ipynb

@@ -4406,7 +4406,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.6.9"
   }
  },
  "nbformat": 4,

4_logistic_regression/2-Logistic_regression.ipynb

@@ -698,7 +698,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.6.9"
   }
  },
  "nbformat": 4,