-
-
-
-
-""" Obtain all kinds of attributes of a graph dataset.
-"""
-
-
-[docs]def get_dataset_attributes(Gn,
- target=None,
- attr_names=[],
- node_label=None,
- edge_label=None):
- """Returns the structure and property information of the graph dataset Gn.
-
- Parameters
- ----------
- Gn : List of NetworkX graph
- List of graphs whose information will be returned.
- target : list
- The list of classification targets corresponding to Gn. Only works for
- classification problems.
- attr_names : list
- List of strings which indicate which informations will be returned. The
- possible choices includes:
- 'substructures': sub-structures Gn contains, including 'linear', 'non
- linear' and 'cyclic'.
- 'node_labeled': whether vertices have symbolic labels.
- 'edge_labeled': whether egdes have symbolic labels.
- 'is_directed': whether graphs in Gn are directed.
- 'dataset_size': number of graphs in Gn.
- 'ave_node_num': average number of vertices of graphs in Gn.
- 'min_node_num': minimum number of vertices of graphs in Gn.
- 'max_node_num': maximum number of vertices of graphs in Gn.
- 'ave_edge_num': average number of edges of graphs in Gn.
- 'min_edge_num': minimum number of edges of graphs in Gn.
- 'max_edge_num': maximum number of edges of graphs in Gn.
- 'ave_node_degree': average vertex degree of graphs in Gn.
- 'min_node_degree': minimum vertex degree of graphs in Gn.
- 'max_node_degree': maximum vertex degree of graphs in Gn.
- 'ave_fill_factor': average fill factor (number_of_edges /
- (number_of_nodes ** 2)) of graphs in Gn.
- 'min_fill_factor': minimum fill factor of graphs in Gn.
- 'max_fill_factor': maximum fill factor of graphs in Gn.
- 'node_label_num': number of symbolic vertex labels.
- 'edge_label_num': number of symbolic edge labels.
- 'node_attr_dim': number of dimensions of non-symbolic vertex labels.
- Extracted from the 'attributes' attribute of graph nodes.
- 'edge_attr_dim': number of dimensions of non-symbolic edge labels.
- Extracted from the 'attributes' attribute of graph edges.
- 'class_number': number of classes. Only available for classification
- problems.
- node_label : string
- Node attribute used as label. The default node label is atom. Mandatory
- when 'node_labeled' or 'node_label_num' is required.
- edge_label : string
- Edge attribute used as label. The default edge label is bond_type.
- Mandatory when 'edge_labeled' or 'edge_label_num' is required.
-
- Return
- ------
- attrs : dict
- Value for each property.
- """
- import networkx as nx
- import numpy as np
-
- attrs = {}
-
- def get_dataset_size(Gn):
- return len(Gn)
-
- def get_all_node_num(Gn):
- return [nx.number_of_nodes(G) for G in Gn]
-
- def get_ave_node_num(all_node_num):
- return np.mean(all_node_num)
-
- def get_min_node_num(all_node_num):
- return np.amin(all_node_num)
-
- def get_max_node_num(all_node_num):
- return np.amax(all_node_num)
-
- def get_all_edge_num(Gn):
- return [nx.number_of_edges(G) for G in Gn]
-
- def get_ave_edge_num(all_edge_num):
- return np.mean(all_edge_num)
-
- def get_min_edge_num(all_edge_num):
- return np.amin(all_edge_num)
-
- def get_max_edge_num(all_edge_num):
- return np.amax(all_edge_num)
-
- def is_node_labeled(Gn):
- return False if node_label is None else True
-
- def get_node_label_num(Gn):
- nl = set()
- for G in Gn:
- nl = nl | set(nx.get_node_attributes(G, node_label).values())
- return len(nl)
-
- def is_edge_labeled(Gn):
- return False if edge_label is None else True
-
- def get_edge_label_num(Gn):
- el = set()
- for G in Gn:
- el = el | set(nx.get_edge_attributes(G, edge_label).values())
- return len(el)
-
- def is_directed(Gn):
- return nx.is_directed(Gn[0])
-
- def get_ave_node_degree(Gn):
- return np.mean([np.mean(list(dict(G.degree()).values())) for G in Gn])
-
- def get_max_node_degree(Gn):
- return np.amax([np.mean(list(dict(G.degree()).values())) for G in Gn])
-
- def get_min_node_degree(Gn):
- return np.amin([np.mean(list(dict(G.degree()).values())) for G in Gn])
-
- # get fill factor, the number of non-zero entries in the adjacency matrix.
- def get_ave_fill_factor(Gn):
- return np.mean([nx.number_of_edges(G) / (nx.number_of_nodes(G)
- * nx.number_of_nodes(G)) for G in Gn])
-
- def get_max_fill_factor(Gn):
- return np.amax([nx.number_of_edges(G) / (nx.number_of_nodes(G)
- * nx.number_of_nodes(G)) for G in Gn])
-
- def get_min_fill_factor(Gn):
- return np.amin([nx.number_of_edges(G) / (nx.number_of_nodes(G)
- * nx.number_of_nodes(G)) for G in Gn])
-
- def get_substructures(Gn):
- subs = set()
- for G in Gn:
- degrees = list(dict(G.degree()).values())
- if any(i == 2 for i in degrees):
- subs.add('linear')
- if np.amax(degrees) >= 3:
- subs.add('non linear')
- if 'linear' in subs and 'non linear' in subs:
- break
-
- if is_directed(Gn):
- for G in Gn:
- if len(list(nx.find_cycle(G))) > 0:
- subs.add('cyclic')
- break
- # else:
- # # @todo: this method does not work for big graph with large amount of edges like D&D, try a better way.
- # upper = np.amin([nx.number_of_edges(G) for G in Gn]) * 2 + 10
- # for G in Gn:
- # if (nx.number_of_edges(G) < upper):
- # cyc = list(nx.simple_cycles(G.to_directed()))
- # if any(len(i) > 2 for i in cyc):
- # subs.add('cyclic')
- # break
- # if 'cyclic' not in subs:
- # for G in Gn:
- # cyc = list(nx.simple_cycles(G.to_directed()))
- # if any(len(i) > 2 for i in cyc):
- # subs.add('cyclic')
- # break
-
- return subs
-
- def get_class_num(target):
- return len(set(target))
-
- def get_node_attr_dim(Gn):
- for G in Gn:
- for n in G.nodes(data=True):
- if 'attributes' in n[1]:
- return len(n[1]['attributes'])
- return 0
-
- def get_edge_attr_dim(Gn):
- for G in Gn:
- if nx.number_of_edges(G) > 0:
- for e in G.edges(data=True):
- if 'attributes' in e[2]:
- return len(e[2]['attributes'])
- return 0
-
- if attr_names == []:
- attr_names = [
- 'substructures',
- 'node_labeled',
- 'edge_labeled',
- 'is_directed',
- 'dataset_size',
- 'ave_node_num',
- 'min_node_num',
- 'max_node_num',
- 'ave_edge_num',
- 'min_edge_num',
- 'max_edge_num',
- 'ave_node_degree',
- 'min_node_degree',
- 'max_node_degree',
- 'ave_fill_factor',
- 'min_fill_factor',
- 'max_fill_factor',
- 'node_label_num',
- 'edge_label_num',
- 'node_attr_dim',
- 'edge_attr_dim',
- 'class_number',
- ]
-
- # dataset size
- if 'dataset_size' in attr_names:
-
- attrs.update({'dataset_size': get_dataset_size(Gn)})
-
- # graph node number
- if any(i in attr_names
- for i in ['ave_node_num', 'min_node_num', 'max_node_num']):
-
- all_node_num = get_all_node_num(Gn)
-
- if 'ave_node_num' in attr_names:
-
- attrs.update({'ave_node_num': get_ave_node_num(all_node_num)})
-
- if 'min_node_num' in attr_names:
-
- attrs.update({'min_node_num': get_min_node_num(all_node_num)})
-
- if 'max_node_num' in attr_names:
-
- attrs.update({'max_node_num': get_max_node_num(all_node_num)})
-
- # graph edge number
- if any(i in attr_names for i in
- ['ave_edge_num', 'min_edge_num', 'max_edge_num']):
-
- all_edge_num = get_all_edge_num(Gn)
-
- if 'ave_edge_num' in attr_names:
-
- attrs.update({'ave_edge_num': get_ave_edge_num(all_edge_num)})
-
- if 'max_edge_num' in attr_names:
-
- attrs.update({'max_edge_num': get_max_edge_num(all_edge_num)})
-
- if 'min_edge_num' in attr_names:
-
- attrs.update({'min_edge_num': get_min_edge_num(all_edge_num)})
-
- # label number
- if any(i in attr_names for i in ['node_labeled', 'node_label_num']):
- is_nl = is_node_labeled(Gn)
- node_label_num = get_node_label_num(Gn)
-
- if 'node_labeled' in attr_names:
- # graphs are considered node unlabeled if all nodes have the same label.
- attrs.update({'node_labeled': is_nl if node_label_num > 1 else False})
-
- if 'node_label_num' in attr_names:
- attrs.update({'node_label_num': node_label_num})
-
- if any(i in attr_names for i in ['edge_labeled', 'edge_label_num']):
- is_el = is_edge_labeled(Gn)
- edge_label_num = get_edge_label_num(Gn)
-
- if 'edge_labeled' in attr_names:
- # graphs are considered edge unlabeled if all edges have the same label.
- attrs.update({'edge_labeled': is_el if edge_label_num > 1 else False})
-
- if 'edge_label_num' in attr_names:
- attrs.update({'edge_label_num': edge_label_num})
-
- if 'is_directed' in attr_names:
- attrs.update({'is_directed': is_directed(Gn)})
-
- if 'ave_node_degree' in attr_names:
- attrs.update({'ave_node_degree': get_ave_node_degree(Gn)})
-
- if 'max_node_degree' in attr_names:
- attrs.update({'max_node_degree': get_max_node_degree(Gn)})
-
- if 'min_node_degree' in attr_names:
- attrs.update({'min_node_degree': get_min_node_degree(Gn)})
-
- if 'ave_fill_factor' in attr_names:
- attrs.update({'ave_fill_factor': get_ave_fill_factor(Gn)})
-
- if 'max_fill_factor' in attr_names:
- attrs.update({'max_fill_factor': get_max_fill_factor(Gn)})
-
- if 'min_fill_factor' in attr_names:
- attrs.update({'min_fill_factor': get_min_fill_factor(Gn)})
-
- if 'substructures' in attr_names:
- attrs.update({'substructures': get_substructures(Gn)})
-
- if 'class_number' in attr_names:
- attrs.update({'class_number': get_class_num(target)})
-
- if 'node_attr_dim' in attr_names:
- attrs['node_attr_dim'] = get_node_attr_dim(Gn)
-
- if 'edge_attr_dim' in attr_names:
- attrs['edge_attr_dim'] = get_edge_attr_dim(Gn)
-
- from collections import OrderedDict
- return OrderedDict(
- sorted(attrs.items(), key=lambda i: attr_names.index(i[0])))
-
-""" Utilities function to manage graph files
-"""
-from os.path import dirname, splitext
-
-[docs]def loadCT(filename):
- """load data from a Chemical Table (.ct) file.
-
- Notes
- ------
- a typical example of data in .ct is like this:
-
- 3 2 <- number of nodes and edges
- 0.0000 0.0000 0.0000 C <- each line describes a node (x,y,z + label)
- 0.0000 0.0000 0.0000 C
- 0.0000 0.0000 0.0000 O
- 1 3 1 1 <- each line describes an edge : to, from, bond type, bond stereo
- 2 3 1 1
-
- Check https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=10&ved=2ahUKEwivhaSdjsTlAhVhx4UKHczHA8gQFjAJegQIARAC&url=https%3A%2F%2Fwww.daylight.com%2Fmeetings%2Fmug05%2FKappler%2Fctfile.pdf&usg=AOvVaw1cDNrrmMClkFPqodlF2inS
- for detailed format discription.
- """
- import networkx as nx
- from os.path import basename
- g = nx.Graph()
- with open(filename) as f:
- content = f.read().splitlines()
- g = nx.Graph(
- name = str(content[0]),
- filename = basename(filename)) # set name of the graph
- tmp = content[1].split(" ")
- if tmp[0] == '':
- nb_nodes = int(tmp[1]) # number of the nodes
- nb_edges = int(tmp[2]) # number of the edges
- else:
- nb_nodes = int(tmp[0])
- nb_edges = int(tmp[1])
- # patch for compatibility : label will be removed later
- for i in range(0, nb_nodes):
- tmp = content[i + 2].split(" ")
- tmp = [x for x in tmp if x != '']
- g.add_node(i, atom=tmp[3].strip(),
- label=[item.strip() for item in tmp[3:]],
- attributes=[item.strip() for item in tmp[0:3]])
- for i in range(0, nb_edges):
- tmp = content[i + g.number_of_nodes() + 2].split(" ")
- tmp = [x for x in tmp if x != '']
- g.add_edge(int(tmp[0]) - 1, int(tmp[1]) - 1,
- bond_type=tmp[2].strip(),
- label=[item.strip() for item in tmp[2:]])
- return g
-
-
-[docs]def loadGXL(filename):
- from os.path import basename
- import networkx as nx
- import xml.etree.ElementTree as ET
-
- tree = ET.parse(filename)
- root = tree.getroot()
- index = 0
- g = nx.Graph(filename=basename(filename), name=root[0].attrib['id'])
- dic = {} # used to retrieve incident nodes of edges
- for node in root.iter('node'):
- dic[node.attrib['id']] = index
- labels = {}
- for attr in node.iter('attr'):
- labels[attr.attrib['name']] = attr[0].text
- if 'chem' in labels:
- labels['label'] = labels['chem']
- labels['atom'] = labels['chem']
- g.add_node(index, **labels)
- index += 1
-
- for edge in root.iter('edge'):
- labels = {}
- for attr in edge.iter('attr'):
- labels[attr.attrib['name']] = attr[0].text
- if 'valence' in labels:
- labels['label'] = labels['valence']
- labels['bond_type'] = labels['valence']
- g.add_edge(dic[edge.attrib['from']], dic[edge.attrib['to']], **labels)
- return g
-
-
-[docs]def saveGXL(graph, filename, method='benoit'):
- if method == 'benoit':
- import xml.etree.ElementTree as ET
- root_node = ET.Element('gxl')
- attr = dict()
- attr['id'] = str(graph.graph['name'])
- attr['edgeids'] = 'true'
- attr['edgemode'] = 'undirected'
- graph_node = ET.SubElement(root_node, 'graph', attrib=attr)
-
- for v in graph:
- current_node = ET.SubElement(graph_node, 'node', attrib={'id': str(v)})
- for attr in graph.nodes[v].keys():
- cur_attr = ET.SubElement(
- current_node, 'attr', attrib={'name': attr})
- cur_value = ET.SubElement(cur_attr,
- graph.nodes[v][attr].__class__.__name__)
- cur_value.text = graph.nodes[v][attr]
-
- for v1 in graph:
- for v2 in graph[v1]:
- if (v1 < v2): # Non oriented graphs
- cur_edge = ET.SubElement(
- graph_node,
- 'edge',
- attrib={
- 'from': str(v1),
- 'to': str(v2)
- })
- for attr in graph[v1][v2].keys():
- cur_attr = ET.SubElement(
- cur_edge, 'attr', attrib={'name': attr})
- cur_value = ET.SubElement(
- cur_attr, graph[v1][v2][attr].__class__.__name__)
- cur_value.text = str(graph[v1][v2][attr])
-
- tree = ET.ElementTree(root_node)
- tree.write(filename)
- elif method == 'gedlib':
- # reference: https://github.com/dbblumenthal/gedlib/blob/master/data/generate_molecules.py#L22
-# pass
- gxl_file = open(filename, 'w')
- gxl_file.write("<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n")
- gxl_file.write("<!DOCTYPE gxl SYSTEM \"http://www.gupro.de/GXL/gxl-1.0.dtd\">\n")
- gxl_file.write("<gxl xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n")
- gxl_file.write("<graph id=\"" + str(graph.graph['name']) + "\" edgeids=\"true\" edgemode=\"undirected\">\n")
- for v, attrs in graph.nodes(data=True):
- gxl_file.write("<node id=\"_" + str(v) + "\">")
- gxl_file.write("<attr name=\"" + "chem" + "\"><int>" + str(attrs['chem']) + "</int></attr>")
- gxl_file.write("</node>\n")
- for v1, v2, attrs in graph.edges(data=True):
- gxl_file.write("<edge from=\"_" + str(v1) + "\" to=\"_" + str(v2) + "\">")
- gxl_file.write("<attr name=\"valence\"><int>" + str(attrs['valence']) + "</int></attr>")
-# gxl_file.write("<attr name=\"valence\"><int>" + "1" + "</int></attr>")
- gxl_file.write("</edge>\n")
- gxl_file.write("</graph>\n")
- gxl_file.write("</gxl>")
- gxl_file.close()
- elif method == 'gedlib-letter':
- # reference: https://github.com/dbblumenthal/gedlib/blob/master/data/generate_molecules.py#L22
- # and https://github.com/dbblumenthal/gedlib/blob/master/data/datasets/Letter/HIGH/AP1_0000.gxl
- gxl_file = open(filename, 'w')
- gxl_file.write("<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n")
- gxl_file.write("<!DOCTYPE gxl SYSTEM \"http://www.gupro.de/GXL/gxl-1.0.dtd\">\n")
- gxl_file.write("<gxl xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n")
- gxl_file.write("<graph id=\"" + str(graph.graph['name']) + "\" edgeids=\"false\" edgemode=\"undirected\">\n")
- for v, attrs in graph.nodes(data=True):
- gxl_file.write("<node id=\"_" + str(v) + "\">")
- gxl_file.write("<attr name=\"x\"><float>" + str(attrs['attributes'][0]) + "</float></attr>")
- gxl_file.write("<attr name=\"y\"><float>" + str(attrs['attributes'][1]) + "</float></attr>")
- gxl_file.write("</node>\n")
- for v1, v2, attrs in graph.edges(data=True):
- gxl_file.write("<edge from=\"_" + str(v1) + "\" to=\"_" + str(v2) + "\"/>\n")
- gxl_file.write("</graph>\n")
- gxl_file.write("</gxl>")
- gxl_file.close()
-
-
-[docs]def loadSDF(filename):
- """load data from structured data file (.sdf file).
-
- Notes
- ------
- A SDF file contains a group of molecules, represented in the similar way as in MOL format.
- Check http://www.nonlinear.com/progenesis/sdf-studio/v0.9/faq/sdf-file-format-guidance.aspx, 2018 for detailed structure.
- """
- import networkx as nx
- from os.path import basename
- from tqdm import tqdm
- import sys
- data = []
- with open(filename) as f:
- content = f.read().splitlines()
- index = 0
- pbar = tqdm(total=len(content) + 1, desc='load SDF', file=sys.stdout)
- while index < len(content):
- index_old = index
-
- g = nx.Graph(name=content[index].strip()) # set name of the graph
-
- tmp = content[index + 3]
- nb_nodes = int(tmp[:3]) # number of the nodes
- nb_edges = int(tmp[3:6]) # number of the edges
-
- for i in range(0, nb_nodes):
- tmp = content[i + index + 4]
- g.add_node(i, atom=tmp[31:34].strip())
-
- for i in range(0, nb_edges):
- tmp = content[i + index + g.number_of_nodes() + 4]
- tmp = [tmp[i:i + 3] for i in range(0, len(tmp), 3)]
- g.add_edge(
- int(tmp[0]) - 1, int(tmp[1]) - 1, bond_type=tmp[2].strip())
-
- data.append(g)
-
- index += 4 + g.number_of_nodes() + g.number_of_edges()
- while content[index].strip() != '$$$$': # seperator
- index += 1
- index += 1
-
- pbar.update(index - index_old)
- pbar.update(1)
- pbar.close()
-
- return data
-
-
-[docs]def loadMAT(filename, extra_params):
- """Load graph data from a MATLAB (up to version 7.1) .mat file.
-
- Notes
- ------
- A MAT file contains a struct array containing graphs, and a column vector lx containing a class label for each graph.
- Check README in downloadable file in http://mlcb.is.tuebingen.mpg.de/Mitarbeiter/Nino/WL/, 2018 for detailed structure.
- """
- from scipy.io import loadmat
- import numpy as np
- import networkx as nx
- data = []
- content = loadmat(filename)
- order = extra_params['am_sp_al_nl_el']
- # print(content)
- # print('----')
- for key, value in content.items():
- if key[0] == 'l': # class label
- y = np.transpose(value)[0].tolist()
- # print(y)
- elif key[0] != '_':
- # print(value[0][0][0])
- # print()
- # print(value[0][0][1])
- # print()
- # print(value[0][0][2])
- # print()
- # if len(value[0][0]) > 3:
- # print(value[0][0][3])
- # print('----')
- # if adjacency matrix is not compressed / edge label exists
- if order[1] == 0:
- for i, item in enumerate(value[0]):
- # print(item)
- # print('------')
- g = nx.Graph(name=i) # set name of the graph
- nl = np.transpose(item[order[3]][0][0][0]) # node label
- # print(item[order[3]])
- # print()
- for index, label in enumerate(nl[0]):
- g.add_node(index, atom=str(label))
- el = item[order[4]][0][0][0] # edge label
- for edge in el:
- g.add_edge(
- edge[0] - 1, edge[1] - 1, bond_type=str(edge[2]))
- data.append(g)
- else:
- from scipy.sparse import csc_matrix
- for i, item in enumerate(value[0]):
- # print(item)
- # print('------')
- g = nx.Graph(name=i) # set name of the graph
- nl = np.transpose(item[order[3]][0][0][0]) # node label
- # print(nl)
- # print()
- for index, label in enumerate(nl[0]):
- g.add_node(index, atom=str(label))
- sam = item[order[0]] # sparse adjacency matrix
- index_no0 = sam.nonzero()
- for col, row in zip(index_no0[0], index_no0[1]):
- # print(col)
- # print(row)
- g.add_edge(col, row)
- data.append(g)
- # print(g.edges(data=True))
- return data, y
-
-
-[docs]def loadTXT(dirname_dataset):
- """Load graph data from a .txt file.
-
- Notes
- ------
- The graph data is loaded from separate files.
- Check README in downloadable file http://tiny.cc/PK_MLJ_data, 2018 for detailed structure.
- """
- import numpy as np
- import networkx as nx
- from os import listdir
- from os.path import dirname
-
- # load data file names
- for name in listdir(dirname_dataset):
- if '_A' in name:
- fam = dirname_dataset + '/' + name
- elif '_graph_indicator' in name:
- fgi = dirname_dataset + '/' + name
- elif '_graph_labels' in name:
- fgl = dirname_dataset + '/' + name
- elif '_node_labels' in name:
- fnl = dirname_dataset + '/' + name
- elif '_edge_labels' in name:
- fel = dirname_dataset + '/' + name
- elif '_edge_attributes' in name:
- fea = dirname_dataset + '/' + name
- elif '_node_attributes' in name:
- fna = dirname_dataset + '/' + name
- elif '_graph_attributes' in name:
- fga = dirname_dataset + '/' + name
- # this is supposed to be the node attrs, make sure to put this as the last 'elif'
- elif '_attributes' in name:
- fna = dirname_dataset + '/' + name
-
- content_gi = open(fgi).read().splitlines() # graph indicator
- content_am = open(fam).read().splitlines() # adjacency matrix
- content_gl = open(fgl).read().splitlines() # lass labels
-
- # create graphs and add nodes
- data = [nx.Graph(name=i) for i in range(0, len(content_gl))]
- if 'fnl' in locals():
- content_nl = open(fnl).read().splitlines() # node labels
- for i, line in enumerate(content_gi):
- # transfer to int first in case of unexpected blanks
- data[int(line) - 1].add_node(i, atom=str(int(content_nl[i])))
- else:
- for i, line in enumerate(content_gi):
- data[int(line) - 1].add_node(i)
-
- # add edges
- for line in content_am:
- tmp = line.split(',')
- n1 = int(tmp[0]) - 1
- n2 = int(tmp[1]) - 1
- # ignore edge weight here.
- g = int(content_gi[n1]) - 1
- data[g].add_edge(n1, n2)
-
- # add edge labels
- if 'fel' in locals():
- content_el = open(fel).read().splitlines()
- for index, line in enumerate(content_el):
- label = line.strip()
- n = [int(i) - 1 for i in content_am[index].split(',')]
- g = int(content_gi[n[0]]) - 1
- data[g].edges[n[0], n[1]]['bond_type'] = label
-
- # add node attributes
- if 'fna' in locals():
- content_na = open(fna).read().splitlines()
- for i, line in enumerate(content_na):
- attrs = [i.strip() for i in line.split(',')]
- g = int(content_gi[i]) - 1
- data[g].nodes[i]['attributes'] = attrs
-
- # add edge attributes
- if 'fea' in locals():
- content_ea = open(fea).read().splitlines()
- for index, line in enumerate(content_ea):
- attrs = [i.strip() for i in line.split(',')]
- n = [int(i) - 1 for i in content_am[index].split(',')]
- g = int(content_gi[n[0]]) - 1
- data[g].edges[n[0], n[1]]['attributes'] = attrs
-
- # load y
- y = [int(i) for i in content_gl]
-
- return data, y
-
-
-[docs]def loadDataset(filename, filename_y=None, extra_params=None):
- """Read graph data from filename and load them as NetworkX graphs.
-
- Parameters
- ----------
- filename : string
- The name of the file from where the dataset is read.
- filename_y : string
- The name of file of the targets corresponding to graphs.
- extra_params : dict
- Extra parameters only designated to '.mat' format.
-
- Return
- ------
- data : List of NetworkX graph.
- y : List
- Targets corresponding to graphs.
-
- Notes
- -----
- This function supports following graph dataset formats:
- 'ds': load data from .ds file. See comments of function loadFromDS for a example.
- 'cxl': load data from Graph eXchange Language file (.cxl file). See
- http://www.gupro.de/GXL/Introduction/background.html, 2019 for detail.
- 'sdf': load data from structured data file (.sdf file). See
- http://www.nonlinear.com/progenesis/sdf-studio/v0.9/faq/sdf-file-format-guidance.aspx,
- 2018 for details.
- 'mat': Load graph data from a MATLAB (up to version 7.1) .mat file. See
- README in downloadable file in http://mlcb.is.tuebingen.mpg.de/Mitarbeiter/Nino/WL/,
- 2018 for details.
- 'txt': Load graph data from a special .txt file. See
- https://ls11-www.cs.tu-dortmund.de/staff/morris/graphkerneldatasets,
- 2019 for details. Note here filename is the name of either .txt file in
- the dataset directory.
- """
- extension = splitext(filename)[1][1:]
- if extension == "ds":
- data, y = loadFromDS(filename, filename_y)
- elif extension == "cxl":
- import xml.etree.ElementTree as ET
-
- dirname_dataset = dirname(filename)
- tree = ET.parse(filename)
- root = tree.getroot()
- data = []
- y = []
- for graph in root.iter('graph'):
- mol_filename = graph.attrib['file']
- mol_class = graph.attrib['class']
- data.append(loadGXL(dirname_dataset + '/' + mol_filename))
- y.append(mol_class)
- elif extension == 'xml':
- data, y = loadFromXML(filename, extra_params)
- elif extension == "sdf":
- import numpy as np
- from tqdm import tqdm
- import sys
-
- data = loadSDF(filename)
-
- y_raw = open(filename_y).read().splitlines()
- y_raw.pop(0)
- tmp0 = []
- tmp1 = []
- for i in range(0, len(y_raw)):
- tmp = y_raw[i].split(',')
- tmp0.append(tmp[0])
- tmp1.append(tmp[1].strip())
-
- y = []
- for i in tqdm(range(0, len(data)), desc='ajust data', file=sys.stdout):
- try:
- y.append(tmp1[tmp0.index(data[i].name)].strip())
- except ValueError: # if data[i].name not in tmp0
- data[i] = []
- data = list(filter(lambda a: a != [], data))
- elif extension == "mat":
- data, y = loadMAT(filename, extra_params)
- elif extension == 'txt':
- dirname_dataset = dirname(filename)
- data, y = loadTXT(dirname_dataset)
- # print(len(y))
- # print(y)
- # print(data[0].nodes(data=True))
- # print('----')
- # print(data[0].edges(data=True))
- # for g in data:
- # print(g.nodes(data=True))
- # print('----')
- # print(g.edges(data=True))
-
- return data, y
-
-
-[docs]def loadFromXML(filename, extra_params):
- import xml.etree.ElementTree as ET
-
- if extra_params:
- dirname_dataset = extra_params
- else:
- dirname_dataset = dirname(filename)
- tree = ET.parse(filename)
- root = tree.getroot()
- data = []
- y = []
- for graph in root.iter('graph'):
- mol_filename = graph.attrib['file']
- mol_class = graph.attrib['class']
- data.append(loadGXL(dirname_dataset + '/' + mol_filename))
- y.append(mol_class)
-
- return data, y
-
-
-[docs]def loadFromDS(filename, filename_y):
- """Load data from .ds file.
- Possible graph formats include:
- '.ct': see function loadCT for detail.
- '.gxl': see dunction loadGXL for detail.
- Note these graph formats are checked automatically by the extensions of
- graph files.
- """
- dirname_dataset = dirname(filename)
- data = []
- y = []
- content = open(filename).read().splitlines()
- extension = splitext(content[0].split(' ')[0])[1][1:]
- if filename_y is None or filename_y == '':
- if extension == 'ct':
- for i in range(0, len(content)):
- tmp = content[i].split(' ')
- # remove the '#'s in file names
- data.append(
- loadCT(dirname_dataset + '/' + tmp[0].replace('#', '', 1)))
- y.append(float(tmp[1]))
- elif extension == 'gxl':
- for i in range(0, len(content)):
- tmp = content[i].split(' ')
- # remove the '#'s in file names
- data.append(
- loadGXL(dirname_dataset + '/' + tmp[0].replace('#', '', 1)))
- y.append(float(tmp[1]))
- else: # y in a seperate file
- if extension == 'ct':
- for i in range(0, len(content)):
- tmp = content[i]
- # remove the '#'s in file names
- data.append(
- loadCT(dirname_dataset + '/' + tmp.replace('#', '', 1)))
- elif extension == 'gxl':
- for i in range(0, len(content)):
- tmp = content[i]
- # remove the '#'s in file names
- data.append(
- loadGXL(dirname_dataset + '/' + tmp.replace('#', '', 1)))
-
- content_y = open(filename_y).read().splitlines()
- # assume entries in filename and filename_y have the same order.
- for item in content_y:
- tmp = item.split(' ')
- # assume the 3rd entry in a line is y (for Alkane dataset)
- y.append(float(tmp[2]))
-
- return data, y
-
-
-[docs]def saveDataset(Gn, y, gformat='gxl', group=None, filename='gfile', xparams=None):
- """Save list of graphs.
- """
- import os
- dirname_ds = os.path.dirname(filename)
- if dirname_ds != '':
- dirname_ds += '/'
- if not os.path.exists(dirname_ds) :
- os.makedirs(dirname_ds)
-
- if 'graph_dir' in xparams:
- graph_dir = xparams['graph_dir'] + '/'
- if not os.path.exists(graph_dir):
- os.makedirs(graph_dir)
- else:
- graph_dir = dirname_ds
-
- if group == 'xml' and gformat == 'gxl':
- with open(filename + '.xml', 'w') as fgroup:
- fgroup.write("<?xml version=\"1.0\"?>")
- fgroup.write("\n<!DOCTYPE GraphCollection SYSTEM \"http://www.inf.unibz.it/~blumenthal/dtd/GraphCollection.dtd\">")
- fgroup.write("\n<GraphCollection>")
- for idx, g in enumerate(Gn):
- fname_tmp = "graph" + str(idx) + ".gxl"
- saveGXL(g, graph_dir + fname_tmp, method=xparams['method'])
- fgroup.write("\n\t<graph file=\"" + fname_tmp + "\" class=\"" + str(y[idx]) + "\"/>")
- fgroup.write("\n</GraphCollection>")
- fgroup.close()
-
-
-if __name__ == '__main__':
-# ### Load dataset from .ds file.
-# # .ct files.
-# ds = {'name': 'Alkane', 'dataset': '../../datasets/Alkane/dataset.ds',
-# 'dataset_y': '../../datasets/Alkane/dataset_boiling_point_names.txt'}
-# Gn, y = loadDataset(ds['dataset'], filename_y=ds['dataset_y'])
-## ds = {'name': 'Acyclic', 'dataset': '../../datasets/acyclic/dataset_bps.ds'} # node symb
-## Gn, y = loadDataset(ds['dataset'])
-## ds = {'name': 'MAO', 'dataset': '../../datasets/MAO/dataset.ds'} # node/edge symb
-## Gn, y = loadDataset(ds['dataset'])
-## ds = {'name': 'PAH', 'dataset': '../../datasets/PAH/dataset.ds'} # unlabeled
-## Gn, y = loadDataset(ds['dataset'])
-# print(Gn[1].nodes(data=True))
-# print(Gn[1].edges(data=True))
-# print(y[1])
-
-# # .gxl file.
-# ds = {'name': 'monoterpenoides',
-# 'dataset': '../../datasets/monoterpenoides/dataset_10+.ds'} # node/edge symb
-# Gn, y = loadDataset(ds['dataset'])
-# print(Gn[1].nodes(data=True))
-# print(Gn[1].edges(data=True))
-# print(y[1])
-
- ### Convert graph from one format to another.
- # .gxl file.
- import networkx as nx
- ds = {'name': 'monoterpenoides',
- 'dataset': '../../datasets/monoterpenoides/dataset_10+.ds'} # node/edge symb
- Gn, y = loadDataset(ds['dataset'])
- y = [int(i) for i in y]
- print(Gn[1].nodes(data=True))
- print(Gn[1].edges(data=True))
- print(y[1])
- # Convert a graph to the proper NetworkX format that can be recognized by library gedlib.
- Gn_new = []
- for G in Gn:
- G_new = nx.Graph()
- for nd, attrs in G.nodes(data=True):
- G_new.add_node(str(nd), chem=attrs['atom'])
- for nd1, nd2, attrs in G.edges(data=True):
- G_new.add_edge(str(nd1), str(nd2), valence=attrs['bond_type'])
-# G_new.add_edge(str(nd1), str(nd2))
- Gn_new.append(G_new)
- print(Gn_new[1].nodes(data=True))
- print(Gn_new[1].edges(data=True))
- print(Gn_new[1])
- filename = '/media/ljia/DATA/research-repo/codes/others/gedlib/tests_linlin/generated_datsets/monoterpenoides/gxl/monoterpenoides'
- xparams = {'method': 'gedlib'}
- saveDataset(Gn, y, gformat='gxl', group='xml', filename=filename, xparams=xparams)
-
-# ds = {'name': 'MUTAG', 'dataset': '../../datasets/MUTAG/MUTAG.mat',
-# 'extra_params': {'am_sp_al_nl_el': [0, 0, 3, 1, 2]}} # node/edge symb
-# Gn, y = loadDataset(ds['dataset'], extra_params=ds['extra_params'])
-# saveDataset(Gn, y, group='xml', filename='temp/temp')
-
-""" Functions for python system.
-"""
-
-[docs]def isNotebook():
- """check if code is executed in the IPython notebook.
- """
- try:
- shell = get_ipython().__class__.__name__
- if shell == 'ZMQInteractiveShell':
- return True # Jupyter notebook or qtconsole
- elif shell == 'TerminalInteractiveShell':
- return False # Terminal running IPython
- else:
- return False # Other type (?)
- except NameError:
- return False # Probably standard Python interpreter
-
-"""Those who are not graph kernels. We can be kernels for nodes or edges!
-These kernels are defined between pairs of vectors.
-"""
-import numpy as np
-
-[docs]def deltakernel(x, y):
- """Delta kernel. Return 1 if x == y, 0 otherwise.
-
- Parameters
- ----------
- x, y : any
- Two parts to compare.
-
- Return
- ------
- kernel : integer
- Delta kernel.
-
- References
- ----------
- [1] H. Kashima, K. Tsuda, and A. Inokuchi. Marginalized kernels between
- labeled graphs. In Proceedings of the 20th International Conference on
- Machine Learning, Washington, DC, United States, 2003.
- """
- return x == y #(1 if condition else 0)
-
-
-[docs]def gaussiankernel(x, y, gamma=None):
- """Gaussian kernel.
- Compute the rbf (gaussian) kernel between x and y:
-
- K(x, y) = exp(-gamma ||x-y||^2).
-
- Read more in the :ref:`User Guide <rbf_kernel>`.
-
- Parameters
- ----------
- x, y : array
-
- gamma : float, default None
- If None, defaults to 1.0 / n_features
-
- Returns
- -------
- kernel : float
- """
- if gamma is None:
- gamma = 1.0 / len(x)
-
- xt = np.array([float(itm) for itm in x])
- yt = np.array([float(itm) for itm in y])
- kernel = xt - yt
- kernel = kernel ** 2
- kernel = np.sum(kernel)
- kernel *= -gamma
- kernel = np.exp(kernel)
- return kernel
-
-
-[docs]def polynomialkernel(x, y, d=1, c=0):
- """Polynomial kernel.
- Compute the polynomial kernel between x and y:
-
- K(x, y) = <x, y> ^d + c.
-
- Parameters
- ----------
- x, y : array
-
- d : integer, default 1
-
- c : float, default 0
-
- Returns
- -------
- kernel : float
- """
- return np.dot(x, y) ** d + c
-
-
-[docs]def linearkernel(x, y):
- """Polynomial kernel.
- Compute the polynomial kernel between x and y:
-
- K(x, y) = <x, y>.
-
- Parameters
- ----------
- x, y : array
-
- d : integer, default 1
-
- c : float, default 0
-
- Returns
- -------
- kernel : float
- """
- return np.dot(x, y)
-
-
-[docs]def kernelsum(k1, k2, d11, d12, d21=None, d22=None, lamda1=1, lamda2=1):
- """Sum of a pair of kernels.
-
- k = lamda1 * k1(d11, d12) + lamda2 * k2(d21, d22)
-
- Parameters
- ----------
- k1, k2 : function
- A pair of kernel functions.
- d11, d12:
- Inputs of k1. If d21 or d22 is None, apply d11, d12 to both k1 and k2.
- d21, d22:
- Inputs of k2.
- lamda1, lamda2: float
- Coefficients of the product.
-
- Return
- ------
- kernel : integer
-
- """
- if d21 == None or d22 == None:
- kernel = lamda1 * k1(d11, d12) + lamda2 * k2(d11, d12)
- else:
- kernel = lamda1 * k1(d11, d12) + lamda2 * k2(d21, d22)
- return kernel
-
-
-[docs]def kernelproduct(k1, k2, d11, d12, d21=None, d22=None, lamda=1):
- """Product of a pair of kernels.
-
- k = lamda * k1(d11, d12) * k2(d21, d22)
-
- Parameters
- ----------
- k1, k2 : function
- A pair of kernel functions.
- d11, d12:
- Inputs of k1. If d21 or d22 is None, apply d11, d12 to both k1 and k2.
- d21, d22:
- Inputs of k2.
- lamda: float
- Coefficient of the product.
-
- Return
- ------
- kernel : integer
- """
- if d21 == None or d22 == None:
- kernel = lamda * k1(d11, d12) * k2(d11, d12)
- else:
- kernel = lamda * k1(d11, d12) * k2(d21, d22)
- return kernel
-
-
-if __name__ == '__main__':
- o = polynomialkernel([1, 2], [3, 4], 2, 3)
-
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Fri Nov 8 14:21:25 2019
-
-@author: ljia
-"""
-
-import sys
-import time
-
-[docs]class Logger(object):
- def __init__(self):
- self.terminal = sys.stdout
- self.log = open("log." + str(time.time()) + ".log", "a")
-
-
-
-[docs] def flush(self):
- #this flush method is needed for python 3 compatibility.
- #this handles the flush command by doing nothing.
- #you might want to specify some extra behavior here.
- pass
-
-sys.stdout = Logger()
-
-import numpy as np
-import matplotlib
-matplotlib.use('Agg')
-from matplotlib import pyplot as plt
-from sklearn.kernel_ridge import KernelRidge
-from sklearn.svm import SVC
-from sklearn.metrics import accuracy_score, mean_squared_error
-from sklearn.model_selection import KFold, train_test_split, ParameterGrid
-
-#from joblib import Parallel, delayed
-from multiprocessing import Pool, Array
-from functools import partial
-import sys
-sys.path.insert(0, "../")
-import os
-import time
-import datetime
-#from os.path import basename, splitext
-from pygraph.utils.graphfiles import loadDataset
-from tqdm import tqdm
-
-#from memory_profiler import profile
-
-#@profile
-[docs]def model_selection_for_precomputed_kernel(datafile,
- estimator,
- param_grid_precomputed,
- param_grid,
- model_type,
- NUM_TRIALS=30,
- datafile_y=None,
- extra_params=None,
- ds_name='ds-unknown',
- n_jobs=1,
- read_gm_from_file=False,
- verbose=True):
- """Perform model selection, fitting and testing for precomputed kernels
- using nested CV. Print out neccessary data during the process then finally
- the results.
-
- Parameters
- ----------
- datafile : string
- Path of dataset file.
- estimator : function
- kernel function used to estimate. This function needs to return a gram matrix.
- param_grid_precomputed : dictionary
- Dictionary with names (string) of parameters used to calculate gram
- matrices as keys and lists of parameter settings to try as values. This
- enables searching over any sequence of parameter settings. Params with
- length 1 will be omitted.
- param_grid : dictionary
- Dictionary with names (string) of parameters used as penelties as keys
- and lists of parameter settings to try as values. This enables
- searching over any sequence of parameter settings. Params with length 1
- will be omitted.
- model_type : string
- Type of the problem, can be 'regression' or 'classification'.
- NUM_TRIALS : integer
- Number of random trials of outer cv loop. The default is 30.
- datafile_y : string
- Path of file storing y data. This parameter is optional depending on
- the given dataset file.
- extra_params : dict
- Extra parameters for loading dataset. See function pygraph.utils.
- graphfiles.loadDataset for detail.
- ds_name : string
- Name of the dataset.
- n_jobs : int
- Number of jobs for parallelization.
- read_gm_from_file : boolean
- Whether gram matrices are loaded from a file.
-
- Examples
- --------
- >>> import numpy as np
- >>> import sys
- >>> sys.path.insert(0, "../")
- >>> from pygraph.utils.model_selection_precomputed import model_selection_for_precomputed_kernel
- >>> from pygraph.kernels.untilHPathKernel import untilhpathkernel
- >>>
- >>> datafile = '../datasets/MUTAG/MUTAG_A.txt'
- >>> estimator = untilhpathkernel
- >>> param_grid_precomputed = {’depth’: np.linspace(1, 10, 10), ’k_func’:
- [’MinMax’, ’tanimoto’], ’compute_method’: [’trie’]}
- >>> # ’C’ for classification problems and ’alpha’ for regression problems.
- >>> param_grid = [{’C’: np.logspace(-10, 10, num=41, base=10)}, {’alpha’:
- np.logspace(-10, 10, num=41, base=10)}]
- >>>
- >>> model_selection_for_precomputed_kernel(datafile, estimator,
- param_grid_precomputed, param_grid[0], 'classification', ds_name=’MUTAG’)
- """
- tqdm.monitor_interval = 0
-
- results_dir = '../notebooks/results/' + estimator.__name__
- if not os.path.exists(results_dir):
- os.makedirs(results_dir)
- # a string to save all the results.
- str_fw = '###################### log time: ' + datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") + '. ######################\n\n'
- str_fw += '# This file contains results of ' + estimator.__name__ + ' on dataset ' + ds_name + ',\n# including gram matrices, serial numbers for gram matrix figures and performance.\n\n'
-
- # setup the model type
- model_type = model_type.lower()
- if model_type != 'regression' and model_type != 'classification':
- raise Exception(
- 'The model type is incorrect! Please choose from regression or classification.'
- )
- if verbose:
- print()
- print('--- This is a %s problem ---' % model_type)
- str_fw += 'This is a %s problem.\n' % model_type
-
- # calculate gram matrices rather than read them from file.
- if read_gm_from_file == False:
- # Load the dataset
- if verbose:
- print()
- print('\n1. Loading dataset from file...')
- if isinstance(datafile, str):
- dataset, y_all = loadDataset(
- datafile, filename_y=datafile_y, extra_params=extra_params)
- else: # load data directly from variable.
- dataset = datafile
- y_all = datafile_y
-
- # import matplotlib.pyplot as plt
- # import networkx as nx
- # nx.draw_networkx(dataset[30])
- # plt.show()
-
- # Grid of parameters with a discrete number of values for each.
- param_list_precomputed = list(ParameterGrid(param_grid_precomputed))
- param_list = list(ParameterGrid(param_grid))
-
- gram_matrices = [
- ] # a list to store gram matrices for all param_grid_precomputed
- gram_matrix_time = [
- ] # a list to store time to calculate gram matrices
- param_list_pre_revised = [
- ] # list to store param grids precomputed ignoring the useless ones
-
- # calculate all gram matrices
- if verbose:
- print()
- print('2. Calculating gram matrices. This could take a while...')
- str_fw += '\nII. Gram matrices.\n\n'
- tts = time.time() # start training time
- nb_gm_ignore = 0 # the number of gram matrices those should not be considered, as they may contain elements that are not numbers (NaN)
- for idx, params_out in enumerate(param_list_precomputed):
- y = y_all[:]
- params_out['n_jobs'] = n_jobs
- params_out['verbose'] = verbose
-# print(dataset)
-# import networkx as nx
-# nx.draw_networkx(dataset[1])
-# plt.show()
- rtn_data = estimator(dataset[:], **params_out)
- Kmatrix = rtn_data[0]
- current_run_time = rtn_data[1]
- # for some kernels, some graphs in datasets may not meet the
- # kernels' requirements for graph structure. These graphs are trimmed.
- if len(rtn_data) == 3:
- idx_trim = rtn_data[2] # the index of trimmed graph list
- y = [y[idxt] for idxt in idx_trim] # trim y accordingly
-# Kmatrix = np.random.rand(2250, 2250)
-# current_run_time = 0.1
-
- # remove graphs whose kernels with themselves are zeros
- # @todo: y not changed accordingly?
- Kmatrix_diag = Kmatrix.diagonal().copy()
- nb_g_ignore = 0
- for idxk, diag in enumerate(Kmatrix_diag):
- if diag == 0:
- Kmatrix = np.delete(Kmatrix, (idxk - nb_g_ignore), axis=0)
- Kmatrix = np.delete(Kmatrix, (idxk - nb_g_ignore), axis=1)
- nb_g_ignore += 1
- # normalization
- # @todo: works only for undirected graph?
- Kmatrix_diag = Kmatrix.diagonal().copy()
- for i in range(len(Kmatrix)):
- for j in range(i, len(Kmatrix)):
- Kmatrix[i][j] /= np.sqrt(Kmatrix_diag[i] * Kmatrix_diag[j])
- Kmatrix[j][i] = Kmatrix[i][j]
- if verbose:
- print()
- if params_out == {}:
- if verbose:
- print('the gram matrix is: ')
- str_fw += 'the gram matrix is:\n\n'
- else:
- if verbose:
- print('the gram matrix with parameters', params_out, 'is: \n\n')
- str_fw += 'the gram matrix with parameters %s is:\n\n' % params_out
- if len(Kmatrix) < 2:
- nb_gm_ignore += 1
- if verbose:
- print('ignored, as at most only one of all its diagonal value is non-zero.')
- str_fw += 'ignored, as at most only one of all its diagonal value is non-zero.\n\n'
- else:
- if np.isnan(Kmatrix).any(
- ): # if the matrix contains elements that are not numbers
- nb_gm_ignore += 1
- if verbose:
- print('ignored, as it contains elements that are not numbers.')
- str_fw += 'ignored, as it contains elements that are not numbers.\n\n'
- else:
-# print(Kmatrix)
- str_fw += np.array2string(
- Kmatrix,
- separator=',') + '\n\n'
-# separator=',',
-# threshold=np.inf,
-# floatmode='unique') + '\n\n'
-
- fig_file_name = results_dir + '/GM[ds]' + ds_name
- if params_out != {}:
- fig_file_name += '[params]' + str(idx)
- plt.imshow(Kmatrix)
- plt.colorbar()
- plt.savefig(fig_file_name + '.eps', format='eps', dpi=300)
-# plt.show()
- plt.clf()
- gram_matrices.append(Kmatrix)
- gram_matrix_time.append(current_run_time)
- param_list_pre_revised.append(params_out)
- if nb_g_ignore > 0:
- if verbose:
- print(', where %d graphs are ignored as their graph kernels with themselves are zeros.' % nb_g_ignore)
- str_fw += ', where %d graphs are ignored as their graph kernels with themselves are zeros.' % nb_g_ignore
- if verbose:
- print()
- print(
- '{} gram matrices are calculated, {} of which are ignored.'.format(
- len(param_list_precomputed), nb_gm_ignore))
- str_fw += '{} gram matrices are calculated, {} of which are ignored.\n\n'.format(len(param_list_precomputed), nb_gm_ignore)
- str_fw += 'serial numbers of gram matrix figures and their corresponding parameters settings:\n\n'
- str_fw += ''.join([
- '{}: {}\n'.format(idx, params_out)
- for idx, params_out in enumerate(param_list_precomputed)
- ])
-
- if verbose:
- print()
- if len(gram_matrices) == 0:
- if verbose:
- print('all gram matrices are ignored, no results obtained.')
- str_fw += '\nall gram matrices are ignored, no results obtained.\n\n'
- else:
- # save gram matrices to file.
-# np.savez(results_dir + '/' + ds_name + '.gm',
-# gms=gram_matrices, params=param_list_pre_revised, y=y,
-# gmtime=gram_matrix_time)
- if verbose:
- print(
- '3. Fitting and predicting using nested cross validation. This could really take a while...'
- )
-
- # ---- use pool.imap_unordered to parallel and track progress. ----
-# train_pref = []
-# val_pref = []
-# test_pref = []
-# def func_assign(result, var_to_assign):
-# for idx, itm in enumerate(var_to_assign):
-# itm.append(result[idx])
-# trial_do_partial = partial(trial_do, param_list_pre_revised, param_list, y, model_type)
-#
-# parallel_me(trial_do_partial, range(NUM_TRIALS), func_assign,
-# [train_pref, val_pref, test_pref], glbv=gram_matrices,
-# method='imap_unordered', n_jobs=n_jobs, chunksize=1,
-# itr_desc='cross validation')
-
- def init_worker(gms_toshare):
- global G_gms
- G_gms = gms_toshare
-
-# gram_matrices = np.array(gram_matrices)
-# gms_shape = gram_matrices.shape
-# gms_array = Array('d', np.reshape(gram_matrices.copy(), -1, order='C'))
-# pool = Pool(processes=n_jobs, initializer=init_worker, initargs=(gms_array, gms_shape))
- pool = Pool(processes=n_jobs, initializer=init_worker, initargs=(gram_matrices,))
- trial_do_partial = partial(parallel_trial_do, param_list_pre_revised, param_list, y, model_type)
- train_pref = []
- val_pref = []
- test_pref = []
-# if NUM_TRIALS < 1000 * n_jobs:
-# chunksize = int(NUM_TRIALS / n_jobs) + 1
-# else:
-# chunksize = 1000
- chunksize = 1
- if verbose:
- iterator = tqdm(pool.imap_unordered(trial_do_partial,
- range(NUM_TRIALS), chunksize), desc='cross validation', file=sys.stdout)
- else:
- iterator = pool.imap_unordered(trial_do_partial, range(NUM_TRIALS), chunksize)
- for o1, o2, o3 in iterator:
- train_pref.append(o1)
- val_pref.append(o2)
- test_pref.append(o3)
- pool.close()
- pool.join()
-
-# # ---- use pool.map to parallel. ----
-# pool = Pool(n_jobs)
-# trial_do_partial = partial(trial_do, param_list_pre_revised, param_list, gram_matrices, y[0:250], model_type)
-# result_perf = pool.map(trial_do_partial, range(NUM_TRIALS))
-# train_pref = [item[0] for item in result_perf]
-# val_pref = [item[1] for item in result_perf]
-# test_pref = [item[2] for item in result_perf]
-
-# # ---- direct running, normally use a single CPU core. ----
-# train_pref = []
-# val_pref = []
-# test_pref = []
-# for i in tqdm(range(NUM_TRIALS), desc='cross validation', file=sys.stdout):
-# o1, o2, o3 = trial_do(param_list_pre_revised, param_list, gram_matrices, y, model_type, i)
-# train_pref.append(o1)
-# val_pref.append(o2)
-# test_pref.append(o3)
-# print()
-
- if verbose:
- print()
- print('4. Getting final performance...')
- str_fw += '\nIII. Performance.\n\n'
- # averages and confidences of performances on outer trials for each combination of parameters
- average_train_scores = np.mean(train_pref, axis=0)
-# print('val_pref: ', val_pref[0][0])
- average_val_scores = np.mean(val_pref, axis=0)
-# print('test_pref: ', test_pref[0][0])
- average_perf_scores = np.mean(test_pref, axis=0)
- # sample std is used here
- std_train_scores = np.std(train_pref, axis=0, ddof=1)
- std_val_scores = np.std(val_pref, axis=0, ddof=1)
- std_perf_scores = np.std(test_pref, axis=0, ddof=1)
-
- if model_type == 'regression':
- best_val_perf = np.amin(average_val_scores)
- else:
- best_val_perf = np.amax(average_val_scores)
-# print('average_val_scores: ', average_val_scores)
-# print('best_val_perf: ', best_val_perf)
-# print()
- best_params_index = np.where(average_val_scores == best_val_perf)
- # find smallest val std with best val perf.
- best_val_stds = [
- std_val_scores[value][best_params_index[1][idx]]
- for idx, value in enumerate(best_params_index[0])
- ]
- min_val_std = np.amin(best_val_stds)
- best_params_index = np.where(std_val_scores == min_val_std)
- best_params_out = [
- param_list_pre_revised[i] for i in best_params_index[0]
- ]
- best_params_in = [param_list[i] for i in best_params_index[1]]
- if verbose:
- print('best_params_out: ', best_params_out)
- print('best_params_in: ', best_params_in)
- print()
- print('best_val_perf: ', best_val_perf)
- print('best_val_std: ', min_val_std)
- str_fw += 'best settings of hyper-params to build gram matrix: %s\n' % best_params_out
- str_fw += 'best settings of other hyper-params: %s\n\n' % best_params_in
- str_fw += 'best_val_perf: %s\n' % best_val_perf
- str_fw += 'best_val_std: %s\n' % min_val_std
-
-# print(best_params_index)
-# print(best_params_index[0])
-# print(average_perf_scores)
- final_performance = [
- average_perf_scores[value][best_params_index[1][idx]]
- for idx, value in enumerate(best_params_index[0])
- ]
- final_confidence = [
- std_perf_scores[value][best_params_index[1][idx]]
- for idx, value in enumerate(best_params_index[0])
- ]
- if verbose:
- print('final_performance: ', final_performance)
- print('final_confidence: ', final_confidence)
- str_fw += 'final_performance: %s\n' % final_performance
- str_fw += 'final_confidence: %s\n' % final_confidence
- train_performance = [
- average_train_scores[value][best_params_index[1][idx]]
- for idx, value in enumerate(best_params_index[0])
- ]
- train_std = [
- std_train_scores[value][best_params_index[1][idx]]
- for idx, value in enumerate(best_params_index[0])
- ]
- if verbose:
- print('train_performance: %s' % train_performance)
- print('train_std: ', train_std)
- str_fw += 'train_performance: %s\n' % train_performance
- str_fw += 'train_std: %s\n\n' % train_std
-
- if verbose:
- print()
- tt_total = time.time() - tts # training time for all hyper-parameters
- average_gram_matrix_time = np.mean(gram_matrix_time)
- std_gram_matrix_time = np.std(gram_matrix_time, ddof=1) if len(gram_matrix_time) > 1 else 0
- best_gram_matrix_time = [
- gram_matrix_time[i] for i in best_params_index[0]
- ]
- ave_bgmt = np.mean(best_gram_matrix_time)
- std_bgmt = np.std(best_gram_matrix_time, ddof=1) if len(best_gram_matrix_time) > 1 else 0
- if verbose:
- print('time to calculate gram matrix with different hyper-params: {:.2f}±{:.2f}s'
- .format(average_gram_matrix_time, std_gram_matrix_time))
- print('time to calculate best gram matrix: {:.2f}±{:.2f}s'.format(
- ave_bgmt, std_bgmt))
- print('total training time with all hyper-param choices: {:.2f}s'.format(
- tt_total))
- str_fw += 'time to calculate gram matrix with different hyper-params: {:.2f}±{:.2f}s\n'.format(average_gram_matrix_time, std_gram_matrix_time)
- str_fw += 'time to calculate best gram matrix: {:.2f}±{:.2f}s\n'.format(ave_bgmt, std_bgmt)
- str_fw += 'total training time with all hyper-param choices: {:.2f}s\n\n'.format(tt_total)
-
- # # save results to file
- # np.savetxt(results_name_pre + 'average_train_scores.dt',
- # average_train_scores)
- # np.savetxt(results_name_pre + 'average_val_scores', average_val_scores)
- # np.savetxt(results_name_pre + 'average_perf_scores.dt',
- # average_perf_scores)
- # np.savetxt(results_name_pre + 'std_train_scores.dt', std_train_scores)
- # np.savetxt(results_name_pre + 'std_val_scores.dt', std_val_scores)
- # np.savetxt(results_name_pre + 'std_perf_scores.dt', std_perf_scores)
-
- # np.save(results_name_pre + 'best_params_index', best_params_index)
- # np.save(results_name_pre + 'best_params_pre.dt', best_params_out)
- # np.save(results_name_pre + 'best_params_in.dt', best_params_in)
- # np.save(results_name_pre + 'best_val_perf.dt', best_val_perf)
- # np.save(results_name_pre + 'best_val_std.dt', best_val_std)
- # np.save(results_name_pre + 'final_performance.dt', final_performance)
- # np.save(results_name_pre + 'final_confidence.dt', final_confidence)
- # np.save(results_name_pre + 'train_performance.dt', train_performance)
- # np.save(results_name_pre + 'train_std.dt', train_std)
-
- # np.save(results_name_pre + 'gram_matrix_time.dt', gram_matrix_time)
- # np.save(results_name_pre + 'average_gram_matrix_time.dt',
- # average_gram_matrix_time)
- # np.save(results_name_pre + 'std_gram_matrix_time.dt',
- # std_gram_matrix_time)
- # np.save(results_name_pre + 'best_gram_matrix_time.dt',
- # best_gram_matrix_time)
-
- # read gram matrices from file.
- else:
- # Grid of parameters with a discrete number of values for each.
-# param_list_precomputed = list(ParameterGrid(param_grid_precomputed))
- param_list = list(ParameterGrid(param_grid))
-
- # read gram matrices from file.
- if verbose:
- print()
- print('2. Reading gram matrices from file...')
- str_fw += '\nII. Gram matrices.\n\nGram matrices are read from file, see last log for detail.\n'
- gmfile = np.load(results_dir + '/' + ds_name + '.gm.npz')
- gram_matrices = gmfile['gms'] # a list to store gram matrices for all param_grid_precomputed
- gram_matrix_time = gmfile['gmtime'] # time used to compute the gram matrices
- param_list_pre_revised = gmfile['params'] # list to store param grids precomputed ignoring the useless ones
- y = gmfile['y'].tolist()
-
- tts = time.time() # start training time
-# nb_gm_ignore = 0 # the number of gram matrices those should not be considered, as they may contain elements that are not numbers (NaN)
- if verbose:
- print(
- '3. Fitting and predicting using nested cross validation. This could really take a while...'
- )
-
- # ---- use pool.imap_unordered to parallel and track progress. ----
- def init_worker(gms_toshare):
- global G_gms
- G_gms = gms_toshare
-
- pool = Pool(processes=n_jobs, initializer=init_worker, initargs=(gram_matrices,))
- trial_do_partial = partial(parallel_trial_do, param_list_pre_revised, param_list, y, model_type)
- train_pref = []
- val_pref = []
- test_pref = []
- chunksize = 1
- if verbose:
- iterator = tqdm(pool.imap_unordered(trial_do_partial,
- range(NUM_TRIALS), chunksize), desc='cross validation', file=sys.stdout)
- else:
- iterator = pool.imap_unordered(trial_do_partial, range(NUM_TRIALS), chunksize)
- for o1, o2, o3 in iterator:
- train_pref.append(o1)
- val_pref.append(o2)
- test_pref.append(o3)
- pool.close()
- pool.join()
-
- # # ---- use pool.map to parallel. ----
- # result_perf = pool.map(trial_do_partial, range(NUM_TRIALS))
- # train_pref = [item[0] for item in result_perf]
- # val_pref = [item[1] for item in result_perf]
- # test_pref = [item[2] for item in result_perf]
-
- # # ---- use joblib.Parallel to parallel and track progress. ----
- # trial_do_partial = partial(trial_do, param_list_pre_revised, param_list, gram_matrices, y, model_type)
- # result_perf = Parallel(n_jobs=n_jobs, verbose=10)(delayed(trial_do_partial)(trial) for trial in range(NUM_TRIALS))
- # train_pref = [item[0] for item in result_perf]
- # val_pref = [item[1] for item in result_perf]
- # test_pref = [item[2] for item in result_perf]
-
-# # ---- direct running, normally use a single CPU core. ----
-# train_pref = []
-# val_pref = []
-# test_pref = []
-# for i in tqdm(range(NUM_TRIALS), desc='cross validation', file=sys.stdout):
-# o1, o2, o3 = trial_do(param_list_pre_revised, param_list, gram_matrices, y, model_type, i)
-# train_pref.append(o1)
-# val_pref.append(o2)
-# test_pref.append(o3)
-
- if verbose:
- print()
- print('4. Getting final performance...')
- str_fw += '\nIII. Performance.\n\n'
- # averages and confidences of performances on outer trials for each combination of parameters
- average_train_scores = np.mean(train_pref, axis=0)
- average_val_scores = np.mean(val_pref, axis=0)
- average_perf_scores = np.mean(test_pref, axis=0)
- # sample std is used here
- std_train_scores = np.std(train_pref, axis=0, ddof=1)
- std_val_scores = np.std(val_pref, axis=0, ddof=1)
- std_perf_scores = np.std(test_pref, axis=0, ddof=1)
-
- if model_type == 'regression':
- best_val_perf = np.amin(average_val_scores)
- else:
- best_val_perf = np.amax(average_val_scores)
- best_params_index = np.where(average_val_scores == best_val_perf)
- # find smallest val std with best val perf.
- best_val_stds = [
- std_val_scores[value][best_params_index[1][idx]]
- for idx, value in enumerate(best_params_index[0])
- ]
- min_val_std = np.amin(best_val_stds)
- best_params_index = np.where(std_val_scores == min_val_std)
- best_params_out = [
- param_list_pre_revised[i] for i in best_params_index[0]
- ]
- best_params_in = [param_list[i] for i in best_params_index[1]]
- if verbose:
- print('best_params_out: ', best_params_out)
- print('best_params_in: ', best_params_in)
- print()
- print('best_val_perf: ', best_val_perf)
- print('best_val_std: ', min_val_std)
- str_fw += 'best settings of hyper-params to build gram matrix: %s\n' % best_params_out
- str_fw += 'best settings of other hyper-params: %s\n\n' % best_params_in
- str_fw += 'best_val_perf: %s\n' % best_val_perf
- str_fw += 'best_val_std: %s\n' % min_val_std
-
- final_performance = [
- average_perf_scores[value][best_params_index[1][idx]]
- for idx, value in enumerate(best_params_index[0])
- ]
- final_confidence = [
- std_perf_scores[value][best_params_index[1][idx]]
- for idx, value in enumerate(best_params_index[0])
- ]
- if verbose:
- print('final_performance: ', final_performance)
- print('final_confidence: ', final_confidence)
- str_fw += 'final_performance: %s\n' % final_performance
- str_fw += 'final_confidence: %s\n' % final_confidence
- train_performance = [
- average_train_scores[value][best_params_index[1][idx]]
- for idx, value in enumerate(best_params_index[0])
- ]
- train_std = [
- std_train_scores[value][best_params_index[1][idx]]
- for idx, value in enumerate(best_params_index[0])
- ]
- if verbose:
- print('train_performance: %s' % train_performance)
- print('train_std: ', train_std)
- str_fw += 'train_performance: %s\n' % train_performance
- str_fw += 'train_std: %s\n\n' % train_std
-
- if verbose:
- print()
- average_gram_matrix_time = np.mean(gram_matrix_time)
- std_gram_matrix_time = np.std(gram_matrix_time, ddof=1) if len(gram_matrix_time) > 1 else 0
- best_gram_matrix_time = [
- gram_matrix_time[i] for i in best_params_index[0]
- ]
- ave_bgmt = np.mean(best_gram_matrix_time)
- std_bgmt = np.std(best_gram_matrix_time, ddof=1) if len(best_gram_matrix_time) > 1 else 0
- if verbose:
- print(
- 'time to calculate gram matrix with different hyper-params: {:.2f}±{:.2f}s'
- .format(average_gram_matrix_time, std_gram_matrix_time))
- print('time to calculate best gram matrix: {:.2f}±{:.2f}s'.format(
- ave_bgmt, std_bgmt))
- tt_poster = time.time() - tts # training time with hyper-param choices who did not participate in calculation of gram matrices
- if verbose:
- print(
- 'training time with hyper-param choices who did not participate in calculation of gram matrices: {:.2f}s'.format(
- tt_poster))
- print('total training time with all hyper-param choices: {:.2f}s'.format(
- tt_poster + np.sum(gram_matrix_time)))
-# str_fw += 'time to calculate gram matrix with different hyper-params: {:.2f}±{:.2f}s\n'.format(average_gram_matrix_time, std_gram_matrix_time)
-# str_fw += 'time to calculate best gram matrix: {:.2f}±{:.2f}s\n'.format(ave_bgmt, std_bgmt)
- str_fw += 'training time with hyper-param choices who did not participate in calculation of gram matrices: {:.2f}s\n\n'.format(tt_poster)
-
- # open file to save all results for this dataset.
- if not os.path.exists(results_dir):
- os.makedirs(results_dir)
-
- # print out results as table.
- str_fw += printResultsInTable(param_list, param_list_pre_revised, average_val_scores,
- std_val_scores, average_perf_scores, std_perf_scores,
- average_train_scores, std_train_scores, gram_matrix_time,
- model_type, verbose)
-
- # open file to save all results for this dataset.
- if not os.path.exists(results_dir + '/' + ds_name + '.output.txt'):
- with open(results_dir + '/' + ds_name + '.output.txt', 'w') as f:
- f.write(str_fw)
- else:
- with open(results_dir + '/' + ds_name + '.output.txt', 'r+') as f:
- content = f.read()
- f.seek(0, 0)
- f.write(str_fw + '\n\n\n' + content)
-
-
-[docs]def trial_do(param_list_pre_revised, param_list, gram_matrices, y, model_type, trial): # Test set level
-
-# # get gram matrices from global variables.
-# gram_matrices = np.reshape(G_gms.copy(), G_gms_shape, order='C')
-
- # Arrays to store scores
- train_pref = np.zeros((len(param_list_pre_revised), len(param_list)))
- val_pref = np.zeros((len(param_list_pre_revised), len(param_list)))
- test_pref = np.zeros((len(param_list_pre_revised), len(param_list)))
-
- # randomness added to seeds of split function below. "high" is "size" times
- # 10 so that at least 10 different random output will be yielded. Remove
- # these lines if identical outputs is required.
- rdm_out = np.random.RandomState(seed=None)
- rdm_seed_out_l = rdm_out.uniform(high=len(param_list_pre_revised) * 10,
- size=len(param_list_pre_revised))
-# print(trial, rdm_seed_out_l)
-# print()
- # loop for each outer param tuple
- for index_out, params_out in enumerate(param_list_pre_revised):
- # get gram matrices from global variables.
-# gm_now = G_gms[index_out * G_gms_shape[1] * G_gms_shape[2]:(index_out + 1) * G_gms_shape[1] * G_gms_shape[2]]
-# gm_now = np.reshape(gm_now.copy(), (G_gms_shape[1], G_gms_shape[2]), order='C')
- gm_now = gram_matrices[index_out].copy()
-
- # split gram matrix and y to app and test sets.
- indices = range(len(y))
- # The argument "random_state" in function "train_test_split" can not be
- # set to None, because it will use RandomState instance used by
- # np.random, which is possible for multiple subprocesses to inherit the
- # same seed if they forked at the same time, leading to identical
- # random variates for different subprocesses. Instead, we use "trial"
- # and "index_out" parameters to generate different seeds for different
- # trials/subprocesses and outer loops. "rdm_seed_out_l" is used to add
- # randomness into seeds, so that it yields a different output every
- # time the program is run. To yield identical outputs every time,
- # remove the second line below. Same method is used to the "KFold"
- # function in the inner loop.
- rdm_seed_out = (trial + 1) * (index_out + 1)
- rdm_seed_out = (rdm_seed_out + int(rdm_seed_out_l[index_out])) % (2 ** 32 - 1)
-# print(trial, rdm_seed_out)
- X_app, X_test, y_app, y_test, idx_app, idx_test = train_test_split(
- gm_now, y, indices, test_size=0.1,
- random_state=rdm_seed_out, shuffle=True)
-# print(trial, idx_app, idx_test)
-# print()
- X_app = X_app[:, idx_app]
- X_test = X_test[:, idx_app]
- y_app = np.array(y_app)
- y_test = np.array(y_test)
-
- rdm_seed_in_l = rdm_out.uniform(high=len(param_list) * 10,
- size=len(param_list))
- # loop for each inner param tuple
- for index_in, params_in in enumerate(param_list):
-# if trial == 0:
-# print(index_out, index_in)
-# print('params_in: ', params_in)
-# st = time.time()
- rdm_seed_in = (trial + 1) * (index_out + 1) * (index_in + 1)
-# print("rdm_seed_in1: ", trial, index_in, rdm_seed_in)
- rdm_seed_in = (rdm_seed_in + int(rdm_seed_in_l[index_in])) % (2 ** 32 - 1)
-# print("rdm_seed_in2: ", trial, index_in, rdm_seed_in)
- inner_cv = KFold(n_splits=10, shuffle=True, random_state=rdm_seed_in)
- current_train_perf = []
- current_valid_perf = []
- current_test_perf = []
-
- # For regression use the Kernel Ridge method
-# try:
- if model_type == 'regression':
- kr = KernelRidge(kernel='precomputed', **params_in)
- # loop for each split on validation set level
- # validation set level
- for train_index, valid_index in inner_cv.split(X_app):
-# print("train_index, valid_index: ", trial, index_in, train_index, valid_index)
-# if trial == 0:
-# print('train_index: ', train_index)
-# print('valid_index: ', valid_index)
-# print('idx_test: ', idx_test)
-# print('y_app[train_index]: ', y_app[train_index])
-# print('X_app[train_index, :][:, train_index]: ', X_app[train_index, :][:, train_index])
-# print('X_app[valid_index, :][:, train_index]: ', X_app[valid_index, :][:, train_index])
- kr.fit(X_app[train_index, :][:, train_index],
- y_app[train_index])
-
- # predict on the train, validation and test set
- y_pred_train = kr.predict(
- X_app[train_index, :][:, train_index])
- y_pred_valid = kr.predict(
- X_app[valid_index, :][:, train_index])
-# if trial == 0:
-# print('y_pred_valid: ', y_pred_valid)
-# print()
- y_pred_test = kr.predict(
- X_test[:, train_index])
-
- # root mean squared errors
- current_train_perf.append(
- np.sqrt(
- mean_squared_error(
- y_app[train_index], y_pred_train)))
- current_valid_perf.append(
- np.sqrt(
- mean_squared_error(
- y_app[valid_index], y_pred_valid)))
-# if trial == 0:
-# print(mean_squared_error(
-# y_app[valid_index], y_pred_valid))
- current_test_perf.append(
- np.sqrt(
- mean_squared_error(
- y_test, y_pred_test)))
- # For clcassification use SVM
- else:
- svc = SVC(kernel='precomputed', cache_size=200,
- verbose=False, **params_in)
- # loop for each split on validation set level
- # validation set level
- for train_index, valid_index in inner_cv.split(X_app):
-# np.savez("bug.npy",X_app[train_index, :][:, train_index],y_app[train_index])
-# if trial == 0:
-# print('train_index: ', train_index)
-# print('valid_index: ', valid_index)
-# print('idx_test: ', idx_test)
-# print('y_app[train_index]: ', y_app[train_index])
-# print('X_app[train_index, :][:, train_index]: ', X_app[train_index, :][:, train_index])
-# print('X_app[valid_index, :][:, train_index]: ', X_app[valid_index, :][:, train_index])
- svc.fit(X_app[train_index, :][:, train_index],
- y_app[train_index])
-
- # predict on the train, validation and test set
- y_pred_train = svc.predict(
- X_app[train_index, :][:, train_index])
- y_pred_valid = svc.predict(
- X_app[valid_index, :][:, train_index])
- y_pred_test = svc.predict(
- X_test[:, train_index])
-
- # root mean squared errors
- current_train_perf.append(
- accuracy_score(y_app[train_index],
- y_pred_train))
- current_valid_perf.append(
- accuracy_score(y_app[valid_index],
- y_pred_valid))
- current_test_perf.append(
- accuracy_score(y_test, y_pred_test))
-# except ValueError:
-# print(sys.exc_info()[0])
-# print(params_out, params_in)
-
- # average performance on inner splits
- train_pref[index_out][index_in] = np.mean(
- current_train_perf)
- val_pref[index_out][index_in] = np.mean(
- current_valid_perf)
- test_pref[index_out][index_in] = np.mean(
- current_test_perf)
-# print(time.time() - st)
-# if trial == 0:
-# print('val_pref: ', val_pref)
-# print('test_pref: ', test_pref)
-
- return train_pref, val_pref, test_pref
-
-[docs]def parallel_trial_do(param_list_pre_revised, param_list, y, model_type, trial):
- train_pref, val_pref, test_pref = trial_do(param_list_pre_revised,
- param_list, G_gms, y,
- model_type, trial)
- return train_pref, val_pref, test_pref
-
-
-[docs]def compute_gram_matrices(dataset, y, estimator, param_list_precomputed,
- results_dir, ds_name,
- n_jobs=1, str_fw='', verbose=True):
- gram_matrices = [
- ] # a list to store gram matrices for all param_grid_precomputed
- gram_matrix_time = [
- ] # a list to store time to calculate gram matrices
- param_list_pre_revised = [
- ] # list to store param grids precomputed ignoring the useless ones
-
- nb_gm_ignore = 0 # the number of gram matrices those should not be considered, as they may contain elements that are not numbers (NaN)
- for idx, params_out in enumerate(param_list_precomputed):
- params_out['n_jobs'] = n_jobs
-# print(dataset)
-# import networkx as nx
-# nx.draw_networkx(dataset[1])
-# plt.show()
- rtn_data = estimator(dataset[:], **params_out)
- Kmatrix = rtn_data[0]
- current_run_time = rtn_data[1]
- # for some kernels, some graphs in datasets may not meet the
- # kernels' requirements for graph structure. These graphs are trimmed.
- if len(rtn_data) == 3:
- idx_trim = rtn_data[2] # the index of trimmed graph list
- y = [y[idxt] for idxt in idx_trim] # trim y accordingly
-
- Kmatrix_diag = Kmatrix.diagonal().copy()
- # remove graphs whose kernels with themselves are zeros
- nb_g_ignore = 0
- for idxk, diag in enumerate(Kmatrix_diag):
- if diag == 0:
- Kmatrix = np.delete(Kmatrix, (idxk - nb_g_ignore), axis=0)
- Kmatrix = np.delete(Kmatrix, (idxk - nb_g_ignore), axis=1)
- nb_g_ignore += 1
- # normalization
- for i in range(len(Kmatrix)):
- for j in range(i, len(Kmatrix)):
- Kmatrix[i][j] /= np.sqrt(Kmatrix_diag[i] * Kmatrix_diag[j])
- Kmatrix[j][i] = Kmatrix[i][j]
-
- if verbose:
- print()
- if params_out == {}:
- if verbose:
- print('the gram matrix is: ')
- str_fw += 'the gram matrix is:\n\n'
- else:
- if verbose:
- print('the gram matrix with parameters', params_out, 'is: ')
- str_fw += 'the gram matrix with parameters %s is:\n\n' % params_out
- if len(Kmatrix) < 2:
- nb_gm_ignore += 1
- if verbose:
- print('ignored, as at most only one of all its diagonal value is non-zero.')
- str_fw += 'ignored, as at most only one of all its diagonal value is non-zero.\n\n'
- else:
- if np.isnan(Kmatrix).any(
- ): # if the matrix contains elements that are not numbers
- nb_gm_ignore += 1
- if verbose:
- print('ignored, as it contains elements that are not numbers.')
- str_fw += 'ignored, as it contains elements that are not numbers.\n\n'
- else:
-# print(Kmatrix)
- str_fw += np.array2string(
- Kmatrix,
- separator=',') + '\n\n'
-# separator=',',
-# threshold=np.inf,
-# floatmode='unique') + '\n\n'
-
- fig_file_name = results_dir + '/GM[ds]' + ds_name
- if params_out != {}:
- fig_file_name += '[params]' + str(idx)
- plt.imshow(Kmatrix)
- plt.colorbar()
- plt.savefig(fig_file_name + '.eps', format='eps', dpi=300)
-# plt.show()
- plt.clf()
- gram_matrices.append(Kmatrix)
- gram_matrix_time.append(current_run_time)
- param_list_pre_revised.append(params_out)
- if nb_g_ignore > 0:
- if verbose:
- print(', where %d graphs are ignored as their graph kernels with themselves are zeros.' % nb_g_ignore)
- str_fw += ', where %d graphs are ignored as their graph kernels with themselves are zeros.' % nb_g_ignore
- if verbose:
- print()
- print(
- '{} gram matrices are calculated, {} of which are ignored.'.format(
- len(param_list_precomputed), nb_gm_ignore))
- str_fw += '{} gram matrices are calculated, {} of which are ignored.\n\n'.format(len(param_list_precomputed), nb_gm_ignore)
- str_fw += 'serial numbers of gram matrix figures and their corresponding parameters settings:\n\n'
- str_fw += ''.join([
- '{}: {}\n'.format(idx, params_out)
- for idx, params_out in enumerate(param_list_precomputed)
- ])
-
- return gram_matrices, gram_matrix_time, param_list_pre_revised, y, str_fw
-
-
-[docs]def read_gram_matrices_from_file(results_dir, ds_name):
- gmfile = np.load(results_dir + '/' + ds_name + '.gm.npz')
- gram_matrices = gmfile['gms'] # a list to store gram matrices for all param_grid_precomputed
- param_list_pre_revised = gmfile['params'] # list to store param grids precomputed ignoring the useless ones
- y = gmfile['y'].tolist()
- return gram_matrices, param_list_pre_revised, y
-
-
-[docs]def printResultsInTable(param_list, param_list_pre_revised, average_val_scores,
- std_val_scores, average_perf_scores, std_perf_scores,
- average_train_scores, std_train_scores, gram_matrix_time,
- model_type, verbose):
- from collections import OrderedDict
- from tabulate import tabulate
- table_dict = {}
- if model_type == 'regression':
- for param_in in param_list:
- param_in['alpha'] = '{:.2e}'.format(param_in['alpha'])
- else:
- for param_in in param_list:
- param_in['C'] = '{:.2e}'.format(param_in['C'])
- table_dict['params'] = [{**param_out, **param_in}
- for param_in in param_list for param_out in param_list_pre_revised]
- table_dict['gram_matrix_time'] = [
- '{:.2f}'.format(gram_matrix_time[index_out])
- for param_in in param_list
- for index_out, _ in enumerate(param_list_pre_revised)
- ]
- table_dict['valid_perf'] = [
- '{:.2f}±{:.2f}'.format(average_val_scores[index_out][index_in],
- std_val_scores[index_out][index_in])
- for index_in, _ in enumerate(param_list)
- for index_out, _ in enumerate(param_list_pre_revised)
- ]
- table_dict['test_perf'] = [
- '{:.2f}±{:.2f}'.format(average_perf_scores[index_out][index_in],
- std_perf_scores[index_out][index_in])
- for index_in, _ in enumerate(param_list)
- for index_out, _ in enumerate(param_list_pre_revised)
- ]
- table_dict['train_perf'] = [
- '{:.2f}±{:.2f}'.format(average_train_scores[index_out][index_in],
- std_train_scores[index_out][index_in])
- for index_in, _ in enumerate(param_list)
- for index_out, _ in enumerate(param_list_pre_revised)
- ]
-
- keyorder = [
- 'params', 'train_perf', 'valid_perf', 'test_perf',
- 'gram_matrix_time'
- ]
- if verbose:
- print()
- tb_print = tabulate(OrderedDict(sorted(table_dict.items(),
- key=lambda i: keyorder.index(i[0]))), headers='keys')
-# print(tb_print)
- return 'table of performance v.s. hyper-params:\n\n%s\n\n' % tb_print
-
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Tue Dec 11 11:39:46 2018
-Parallel aid functions.
-@author: ljia
-"""
-import multiprocessing
-from multiprocessing import Pool
-from tqdm import tqdm
-import sys
-
-[docs]def parallel_me(func, func_assign, var_to_assign, itr, len_itr=None, init_worker=None,
- glbv=None, method=None, n_jobs=None, chunksize=None, itr_desc='',
- verbose=True):
- '''
- '''
- if method == 'imap_unordered':
- if glbv: # global varibles required.
-# def init_worker(v_share):
-# global G_var
-# G_var = v_share
- if n_jobs == None:
- n_jobs = multiprocessing.cpu_count()
- with Pool(processes=n_jobs, initializer=init_worker,
- initargs=glbv) as pool:
- if chunksize == None:
- if len_itr < 100 * n_jobs:
- chunksize = int(len_itr / n_jobs) + 1
- else:
- chunksize = 100
- for result in (tqdm(pool.imap_unordered(func, itr, chunksize),
- desc=itr_desc, file=sys.stdout) if verbose else
- pool.imap_unordered(func, itr, chunksize)):
- func_assign(result, var_to_assign)
- else:
- if n_jobs == None:
- n_jobs = multiprocessing.cpu_count()
- with Pool(processes=n_jobs) as pool:
- if chunksize == None:
- if len_itr < 100 * n_jobs:
- chunksize = int(len_itr / n_jobs) + 1
- else:
- chunksize = 100
- for result in (tqdm(pool.imap_unordered(func, itr, chunksize),
- desc=itr_desc, file=sys.stdout) if verbose else
- pool.imap_unordered(func, itr, chunksize)):
- func_assign(result, var_to_assign)
-
-
-
-[docs]def parallel_gm(func, Kmatrix, Gn, init_worker=None, glbv=None,
- method='imap_unordered', n_jobs=None, chunksize=None,
- verbose=True):
- from itertools import combinations_with_replacement
- def func_assign(result, var_to_assign):
- var_to_assign[result[0]][result[1]] = result[2]
- var_to_assign[result[1]][result[0]] = result[2]
- itr = combinations_with_replacement(range(0, len(Gn)), 2)
- len_itr = int(len(Gn) * (len(Gn) + 1) / 2)
- parallel_me(func, func_assign, Kmatrix, itr, len_itr=len_itr,
- init_worker=init_worker, glbv=glbv, method=method, n_jobs=n_jobs,
- chunksize=chunksize, itr_desc='calculating kernels', verbose=verbose)
-
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Wed Jan 30 10:48:49 2019
-
-Trie (prefix tree)
-@author: ljia
-@references:
- https://viblo.asia/p/nlp-build-a-trie-data-structure-from-scratch-with-python-3P0lPzroKox, 2019.1
-"""
-
-import pickle
-import json
-
-""" Trie class
-"""
-[docs]class Trie:
- # init Trie class
- def __init__(self):
- self.root = self.getNode()
-
-
-
-[docs] def insertWord(self, word):
- current = self.root
- for ch in word:
-
- if ch in current["children"]:
- node = current["children"][ch]
- else:
- node = self.getNode()
- current["children"][ch] = node
-
- current = node
- current["isEndOfWord"] = True
- if 'count' in current:
- current['count'] += 1
- else:
- current['count'] = 1
-
-[docs] def searchWord(self, word):
- current = self.root
- for ch in word:
- if ch not in current["children"]:
- return 0
- node = current["children"][ch]
-
- current = node
- if 'count' in current:
- return current["count"]
- else:
- return 0
-
-[docs] def searchWordPrefix(self, word):
- current = self.root
- for ch in word:
- if not current["children"].has_key(ch):
- return False
- node = current["children"][ch]
-
- current = node
- # return True if children contain keys and values
- return bool(current["children"])
-
-
-
- def _delete(self, current, word, index):
- if(index == len(word)):
- if not current["isEndOfWord"]:
- return False
- current["isEndOfWord"] = False
- return len(current["children"].keys()) == 0
-
- ch = word[index]
- if not current["children"].has_key(ch):
- return False
- node = current["children"][ch]
-
- should_delete_current_node = self._delete(node, word, index + 1)
-
- if should_delete_current_node:
- current["children"].pop(ch)
- return len(current["children"].keys()) == 0
-
- return False
-
-[docs] def save_to_pickle(self, file_name):
- f = open(file_name + ".pkl", "wb")
- pickle.dump(self.root, f)
- f.close()
-
-[docs] def load_from_pickle(self, file_name):
- f = open(file_name + ".pkl", "rb")
- self.root = pickle.load(f)
- f.close()
-
-
-
-[docs] def save_to_json(self, file_name):
- json_data = json.dumps(self.root)
- f = open(file_name + ".json", "w")
- f.write(json_data)
- f.close()
-
-[docs] def load_from_json(self, file_name):
- json_file = open(file_name + ".json", "r")
- self.root = json.load(json_file)
- json_file.close()
-
-import networkx as nx
-import numpy as np
-from copy import deepcopy
-#from itertools import product
-
-# from tqdm import tqdm
-
-
-[docs]def getSPLengths(G1):
- sp = nx.shortest_path(G1)
- distances = np.zeros((G1.number_of_nodes(), G1.number_of_nodes()))
- for i in sp.keys():
- for j in sp[i].keys():
- distances[i, j] = len(sp[i][j]) - 1
- return distances
-
-
-[docs]def getSPGraph(G, edge_weight=None):
- """Transform graph G to its corresponding shortest-paths graph.
-
- Parameters
- ----------
- G : NetworkX graph
- The graph to be tramsformed.
- edge_weight : string
- edge attribute corresponding to the edge weight.
-
- Return
- ------
- S : NetworkX graph
- The shortest-paths graph corresponding to G.
-
- Notes
- ------
- For an input graph G, its corresponding shortest-paths graph S contains the same set of nodes as G, while there exists an edge between all nodes in S which are connected by a walk in G. Every edge in S between two nodes is labeled by the shortest distance between these two nodes.
-
- References
- ----------
- [1] Borgwardt KM, Kriegel HP. Shortest-path kernels on graphs. InData Mining, Fifth IEEE International Conference on 2005 Nov 27 (pp. 8-pp). IEEE.
- """
- return floydTransformation(G, edge_weight=edge_weight)
-
-
-[docs]def floydTransformation(G, edge_weight=None):
- """Transform graph G to its corresponding shortest-paths graph using Floyd-transformation.
-
- Parameters
- ----------
- G : NetworkX graph
- The graph to be tramsformed.
- edge_weight : string
- edge attribute corresponding to the edge weight. The default edge weight is bond_type.
-
- Return
- ------
- S : NetworkX graph
- The shortest-paths graph corresponding to G.
-
- References
- ----------
- [1] Borgwardt KM, Kriegel HP. Shortest-path kernels on graphs. InData Mining, Fifth IEEE International Conference on 2005 Nov 27 (pp. 8-pp). IEEE.
- """
- spMatrix = nx.floyd_warshall_numpy(G, weight=edge_weight)
- S = nx.Graph()
- S.add_nodes_from(G.nodes(data=True))
- ns = list(G.nodes())
- for i in range(0, G.number_of_nodes()):
- for j in range(i + 1, G.number_of_nodes()):
- if spMatrix[i, j] != np.inf:
- S.add_edge(ns[i], ns[j], cost=spMatrix[i, j])
- return S
-
-
-[docs]def untotterTransformation(G, node_label, edge_label):
- """Transform graph G according to Mahé et al.'s work to filter out tottering patterns of marginalized kernel and tree pattern kernel.
-
- Parameters
- ----------
- G : NetworkX graph
- The graph to be tramsformed.
- node_label : string
- node attribute used as label. The default node label is 'atom'.
- edge_label : string
- edge attribute used as label. The default edge label is 'bond_type'.
-
- Return
- ------
- gt : NetworkX graph
- The transformed graph corresponding to G.
-
- References
- ----------
- [1] Pierre Mahé, Nobuhisa Ueda, Tatsuya Akutsu, Jean-Luc Perret, and Jean-Philippe Vert. Extensions of marginalized graph kernels. In Proceedings of the twenty-first international conference on Machine learning, page 70. ACM, 2004.
- """
- # arrange all graphs in a list
- G = G.to_directed()
- gt = nx.Graph()
- gt.graph = G.graph
- gt.add_nodes_from(G.nodes(data=True))
- for edge in G.edges():
- gt.add_node(edge)
- gt.node[edge].update({node_label: G.node[edge[1]][node_label]})
- gt.add_edge(edge[0], edge)
- gt.edges[edge[0], edge].update({
- edge_label:
- G[edge[0]][edge[1]][edge_label]
- })
- for neighbor in G[edge[1]]:
- if neighbor != edge[0]:
- gt.add_edge(edge, (edge[1], neighbor))
- gt.edges[edge, (edge[1], neighbor)].update({
- edge_label:
- G[edge[1]][neighbor][edge_label]
- })
- # nx.draw_networkx(gt)
- # plt.show()
-
- # relabel nodes using consecutive integers for convenience of kernel calculation.
- gt = nx.convert_node_labels_to_integers(
- gt, first_label=0, label_attribute='label_orignal')
- return gt
-
-
-[docs]def direct_product(G1, G2, node_label, edge_label):
- """Return the direct/tensor product of directed graphs G1 and G2.
-
- Parameters
- ----------
- G1, G2 : NetworkX graph
- The original graphs.
- node_label : string
- node attribute used as label. The default node label is 'atom'.
- edge_label : string
- edge attribute used as label. The default edge label is 'bond_type'.
-
- Return
- ------
- gt : NetworkX graph
- The direct product graph of G1 and G2.
-
- Notes
- -----
- This method differs from networkx.tensor_product in that this method only adds nodes and edges in G1 and G2 that have the same labels to the direct product graph.
-
- References
- ----------
- [1] Thomas Gärtner, Peter Flach, and Stefan Wrobel. On graph kernels: Hardness results and efficient alternatives. Learning Theory and Kernel Machines, pages 129–143, 2003.
- """
- # arrange all graphs in a list
- from itertools import product
- # G = G.to_directed()
- gt = nx.DiGraph()
- # add nodes
- for u, v in product(G1, G2):
- if G1.nodes[u][node_label] == G2.nodes[v][node_label]:
- gt.add_node((u, v))
- gt.nodes[(u, v)].update({node_label: G1.nodes[u][node_label]})
- # add edges, faster for sparse graphs (no so many edges), which is the most case for now.
- for (u1, v1), (u2, v2) in product(G1.edges, G2.edges):
- if (u1, u2) in gt and (
- v1, v2
- ) in gt and G1.edges[u1, v1][edge_label] == G2.edges[u2,
- v2][edge_label]:
- gt.add_edge((u1, u2), (v1, v2))
- gt.edges[(u1, u2), (v1, v2)].update({
- edge_label:
- G1.edges[u1, v1][edge_label]
- })
-
- # # add edges, faster for dense graphs (a lot of edges, complete graph would be super).
- # for u, v in product(gt, gt):
- # if (u[0], v[0]) in G1.edges and (
- # u[1], v[1]
- # ) in G2.edges and G1.edges[u[0],
- # v[0]][edge_label] == G2.edges[u[1],
- # v[1]][edge_label]:
- # gt.add_edge((u[0], u[1]), (v[0], v[1]))
- # gt.edges[(u[0], u[1]), (v[0], v[1])].update({
- # edge_label:
- # G1.edges[u[0], v[0]][edge_label]
- # })
-
- # relabel nodes using consecutive integers for convenience of kernel calculation.
- # gt = nx.convert_node_labels_to_integers(
- # gt, first_label=0, label_attribute='label_orignal')
- return gt
-
-
-[docs]def graph_deepcopy(G):
- """Deep copy a graph, including deep copy of all nodes, edges and
- attributes of the graph, nodes and edges.
-
- Note
- ----
- It is the same as the NetworkX function graph.copy(), as far as I know.
- """
- # add graph attributes.
- labels = {}
- for k, v in G.graph.items():
- labels[k] = deepcopy(v)
- if G.is_directed():
- G_copy = nx.DiGraph(**labels)
- else:
- G_copy = nx.Graph(**labels)
-
- # add nodes
- for nd, attrs in G.nodes(data=True):
- labels = {}
- for k, v in attrs.items():
- labels[k] = deepcopy(v)
- G_copy.add_node(nd, **labels)
-
- # add edges.
- for nd1, nd2, attrs in G.edges(data=True):
- labels = {}
- for k, v in attrs.items():
- labels[k] = deepcopy(v)
- G_copy.add_edge(nd1, nd2, **labels)
-
- return G_copy
-
-
-[docs]def graph_isIdentical(G1, G2):
- """Check if two graphs are identical, including: same nodes, edges, node
- labels/attributes, edge labels/attributes.
-
- Notes
- ----
- 1. The type of graphs has to be the same.
- 2. Global/Graph attributes are neglected as they may contain names for graphs.
- """
- # check nodes.
- nlist1 = [n for n in G1.nodes(data=True)]
- nlist2 = [n for n in G2.nodes(data=True)]
- if not nlist1 == nlist2:
- return False
- # check edges.
- elist1 = [n for n in G1.edges(data=True)]
- elist2 = [n for n in G2.edges(data=True)]
- if not elist1 == elist2:
- return False
- # check graph attributes.
-
- return True
-
-
-[docs]def get_node_labels(Gn, node_label):
- """Get node labels of dataset Gn.
- """
- nl = set()
- for G in Gn:
- nl = nl | set(nx.get_node_attributes(G, node_label).values())
- return nl
-
-
-[docs]def get_edge_labels(Gn, edge_label):
- """Get edge labels of dataset Gn.
- """
- el = set()
- for G in Gn:
- el = el | set(nx.get_edge_attributes(G, edge_label).values())
- return el
-' + _('Hide Search Matches') + '
') - .appendTo($('#searchbox')); - } - }, - - /** - * init the domain index toggle buttons - */ - initIndexTable : function() { - var togglers = $('img.toggler').click(function() { - var src = $(this).attr('src'); - var idnum = $(this).attr('id').substr(7); - $('tr.cg-' + idnum).toggle(); - if (src.substr(-9) === 'minus.png') - $(this).attr('src', src.substr(0, src.length-9) + 'plus.png'); - else - $(this).attr('src', src.substr(0, src.length-8) + 'minus.png'); - }).css('display', ''); - if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) { - togglers.click(); - } - }, - - /** - * helper function to hide the search marks again - */ - hideSearchWords : function() { - $('#searchbox .highlight-link').fadeOut(300); - $('span.highlighted').removeClass('highlighted'); - }, - - /** - * make the url absolute - */ - makeURL : function(relativeURL) { - return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL; - }, - - /** - * get the current relative url - */ - getCurrentURL : function() { - var path = document.location.pathname; - var parts = path.split(/\//); - $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() { - if (this === '..') - parts.pop(); - }); - var url = parts.join('/'); - return path.substring(url.lastIndexOf('/') + 1, path.length - 1); - }, - - initOnKeyListeners: function() { - $(document).keyup(function(event) { - var activeElementType = document.activeElement.tagName; - // don't navigate when in search box or textarea - if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT') { - switch (event.keyCode) { - case 37: // left - var prevHref = $('link[rel="prev"]').prop('href'); - if (prevHref) { - window.location.href = prevHref; - return false; - } - case 39: // right - var nextHref = $('link[rel="next"]').prop('href'); - if (nextHref) { - window.location.href = nextHref; - return false; - } - } - } - }); - } -}; - -// quick alias for translations -_ = Documentation.gettext; - -$(document).ready(function() { - Documentation.init(); -}); diff --git a/docs/_build/html/_static/documentation_options.js b/docs/_build/html/_static/documentation_options.js deleted file mode 100644 index 168d437..0000000 --- a/docs/_build/html/_static/documentation_options.js +++ /dev/null @@ -1,296 +0,0 @@ -var DOCUMENTATION_OPTIONS = { - URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), - VERSION: '', - LANGUAGE: 'None', - COLLAPSE_INDEX: false, - FILE_SUFFIX: '.html', - HAS_SOURCE: true, - SOURCELINK_SUFFIX: '.txt', - NAVIGATION_WITH_KEYS: false, - SEARCH_LANGUAGE_STOP_WORDS: ["a","and","are","as","at","be","but","by","for","if","in","into","is","it","near","no","not","of","on","or","such","that","the","their","then","there","these","they","this","to","was","will","with"] -}; - - - -/* Non-minified version JS is _stemmer.js if file is provided */ -/** - * Porter Stemmer - */ -var Stemmer = function() { - - var step2list = { - ational: 'ate', - tional: 'tion', - enci: 'ence', - anci: 'ance', - izer: 'ize', - bli: 'ble', - alli: 'al', - entli: 'ent', - eli: 'e', - ousli: 'ous', - ization: 'ize', - ation: 'ate', - ator: 'ate', - alism: 'al', - iveness: 'ive', - fulness: 'ful', - ousness: 'ous', - aliti: 'al', - iviti: 'ive', - biliti: 'ble', - logi: 'log' - }; - - var step3list = { - icate: 'ic', - ative: '', - alize: 'al', - iciti: 'ic', - ical: 'ic', - ful: '', - ness: '' - }; - - var c = "[^aeiou]"; // consonant - var v = "[aeiouy]"; // vowel - var C = c + "[^aeiouy]*"; // consonant sequence - var V = v + "[aeiou]*"; // vowel sequence - - var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0 - var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1 - var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1 - var s_v = "^(" + C + ")?" + v; // vowel in stem - - this.stemWord = function (w) { - var stem; - var suffix; - var firstch; - var origword = w; - - if (w.length < 3) - return w; - - var re; - var re2; - var re3; - var re4; - - firstch = w.substr(0,1); - if (firstch == "y") - w = firstch.toUpperCase() + w.substr(1); - - // Step 1a - re = /^(.+?)(ss|i)es$/; - re2 = /^(.+?)([^s])s$/; - - if (re.test(w)) - w = w.replace(re,"$1$2"); - else if (re2.test(w)) - w = w.replace(re2,"$1$2"); - - // Step 1b - re = /^(.+?)eed$/; - re2 = /^(.+?)(ed|ing)$/; - if (re.test(w)) { - var fp = re.exec(w); - re = new RegExp(mgr0); - if (re.test(fp[1])) { - re = /.$/; - w = w.replace(re,""); - } - } - else if (re2.test(w)) { - var fp = re2.exec(w); - stem = fp[1]; - re2 = new RegExp(s_v); - if (re2.test(stem)) { - w = stem; - re2 = /(at|bl|iz)$/; - re3 = new RegExp("([^aeiouylsz])\\1$"); - re4 = new RegExp("^" + C + v + "[^aeiouwxy]$"); - if (re2.test(w)) - w = w + "e"; - else if (re3.test(w)) { - re = /.$/; - w = w.replace(re,""); - } - else if (re4.test(w)) - w = w + "e"; - } - } - - // Step 1c - re = /^(.+?)y$/; - if (re.test(w)) { - var fp = re.exec(w); - stem = fp[1]; - re = new RegExp(s_v); - if (re.test(stem)) - w = stem + "i"; - } - - // Step 2 - re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/; - if (re.test(w)) { - var fp = re.exec(w); - stem = fp[1]; - suffix = fp[2]; - re = new RegExp(mgr0); - if (re.test(stem)) - w = stem + step2list[suffix]; - } - - // Step 3 - re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/; - if (re.test(w)) { - var fp = re.exec(w); - stem = fp[1]; - suffix = fp[2]; - re = new RegExp(mgr0); - if (re.test(stem)) - w = stem + step3list[suffix]; - } - - // Step 4 - re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/; - re2 = /^(.+?)(s|t)(ion)$/; - if (re.test(w)) { - var fp = re.exec(w); - stem = fp[1]; - re = new RegExp(mgr1); - if (re.test(stem)) - w = stem; - } - else if (re2.test(w)) { - var fp = re2.exec(w); - stem = fp[1] + fp[2]; - re2 = new RegExp(mgr1); - if (re2.test(stem)) - w = stem; - } - - // Step 5 - re = /^(.+?)e$/; - if (re.test(w)) { - var fp = re.exec(w); - stem = fp[1]; - re = new RegExp(mgr1); - re2 = new RegExp(meq1); - re3 = new RegExp("^" + C + v + "[^aeiouwxy]$"); - if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) - w = stem; - } - re = /ll$/; - re2 = new RegExp(mgr1); - if (re.test(w) && re2.test(w)) { - re = /.$/; - w = w.replace(re,""); - } - - // and turn initial Y back to y - if (firstch == "y") - w = firstch.toLowerCase() + w.substr(1); - return w; - } -} - - - - - -var splitChars = (function() { - var result = {}; - var singles = [96, 180, 187, 191, 215, 247, 749, 885, 903, 907, 909, 930, 1014, 1648, - 1748, 1809, 2416, 2473, 2481, 2526, 2601, 2609, 2612, 2615, 2653, 2702, - 2706, 2729, 2737, 2740, 2857, 2865, 2868, 2910, 2928, 2948, 2961, 2971, - 2973, 3085, 3089, 3113, 3124, 3213, 3217, 3241, 3252, 3295, 3341, 3345, - 3369, 3506, 3516, 3633, 3715, 3721, 3736, 3744, 3748, 3750, 3756, 3761, - 3781, 3912, 4239, 4347, 4681, 4695, 4697, 4745, 4785, 4799, 4801, 4823, - 4881, 5760, 5901, 5997, 6313, 7405, 8024, 8026, 8028, 8030, 8117, 8125, - 8133, 8181, 8468, 8485, 8487, 8489, 8494, 8527, 11311, 11359, 11687, 11695, - 11703, 11711, 11719, 11727, 11735, 12448, 12539, 43010, 43014, 43019, 43587, - 43696, 43713, 64286, 64297, 64311, 64317, 64319, 64322, 64325, 65141]; - var i, j, start, end; - for (i = 0; i < singles.length; i++) { - result[singles[i]] = true; - } - var ranges = [[0, 47], [58, 64], [91, 94], [123, 169], [171, 177], [182, 184], [706, 709], - [722, 735], [741, 747], [751, 879], [888, 889], [894, 901], [1154, 1161], - [1318, 1328], [1367, 1368], [1370, 1376], [1416, 1487], [1515, 1519], [1523, 1568], - [1611, 1631], [1642, 1645], [1750, 1764], [1767, 1773], [1789, 1790], [1792, 1807], - [1840, 1868], [1958, 1968], [1970, 1983], [2027, 2035], [2038, 2041], [2043, 2047], - [2070, 2073], [2075, 2083], [2085, 2087], [2089, 2307], [2362, 2364], [2366, 2383], - [2385, 2391], [2402, 2405], [2419, 2424], [2432, 2436], [2445, 2446], [2449, 2450], - [2483, 2485], [2490, 2492], [2494, 2509], [2511, 2523], [2530, 2533], [2546, 2547], - [2554, 2564], [2571, 2574], [2577, 2578], [2618, 2648], [2655, 2661], [2672, 2673], - [2677, 2692], [2746, 2748], [2750, 2767], [2769, 2783], [2786, 2789], [2800, 2820], - [2829, 2830], [2833, 2834], [2874, 2876], [2878, 2907], [2914, 2917], [2930, 2946], - [2955, 2957], [2966, 2968], [2976, 2978], [2981, 2983], [2987, 2989], [3002, 3023], - [3025, 3045], [3059, 3076], [3130, 3132], [3134, 3159], [3162, 3167], [3170, 3173], - [3184, 3191], [3199, 3204], [3258, 3260], [3262, 3293], [3298, 3301], [3312, 3332], - [3386, 3388], [3390, 3423], [3426, 3429], [3446, 3449], [3456, 3460], [3479, 3481], - [3518, 3519], [3527, 3584], [3636, 3647], [3655, 3663], [3674, 3712], [3717, 3718], - [3723, 3724], [3726, 3731], [3752, 3753], [3764, 3772], [3774, 3775], [3783, 3791], - [3802, 3803], [3806, 3839], [3841, 3871], [3892, 3903], [3949, 3975], [3980, 4095], - [4139, 4158], [4170, 4175], [4182, 4185], [4190, 4192], [4194, 4196], [4199, 4205], - [4209, 4212], [4226, 4237], [4250, 4255], [4294, 4303], [4349, 4351], [4686, 4687], - [4702, 4703], [4750, 4751], [4790, 4791], [4806, 4807], [4886, 4887], [4955, 4968], - [4989, 4991], [5008, 5023], [5109, 5120], [5741, 5742], [5787, 5791], [5867, 5869], - [5873, 5887], [5906, 5919], [5938, 5951], [5970, 5983], [6001, 6015], [6068, 6102], - [6104, 6107], [6109, 6111], [6122, 6127], [6138, 6159], [6170, 6175], [6264, 6271], - [6315, 6319], [6390, 6399], [6429, 6469], [6510, 6511], [6517, 6527], [6572, 6592], - [6600, 6607], [6619, 6655], [6679, 6687], [6741, 6783], [6794, 6799], [6810, 6822], - [6824, 6916], [6964, 6980], [6988, 6991], [7002, 7042], [7073, 7085], [7098, 7167], - [7204, 7231], [7242, 7244], [7294, 7400], [7410, 7423], [7616, 7679], [7958, 7959], - [7966, 7967], [8006, 8007], [8014, 8015], [8062, 8063], [8127, 8129], [8141, 8143], - [8148, 8149], [8156, 8159], [8173, 8177], [8189, 8303], [8306, 8307], [8314, 8318], - [8330, 8335], [8341, 8449], [8451, 8454], [8456, 8457], [8470, 8472], [8478, 8483], - [8506, 8507], [8512, 8516], [8522, 8525], [8586, 9311], [9372, 9449], [9472, 10101], - [10132, 11263], [11493, 11498], [11503, 11516], [11518, 11519], [11558, 11567], - [11622, 11630], [11632, 11647], [11671, 11679], [11743, 11822], [11824, 12292], - [12296, 12320], [12330, 12336], [12342, 12343], [12349, 12352], [12439, 12444], - [12544, 12548], [12590, 12592], [12687, 12689], [12694, 12703], [12728, 12783], - [12800, 12831], [12842, 12880], [12896, 12927], [12938, 12976], [12992, 13311], - [19894, 19967], [40908, 40959], [42125, 42191], [42238, 42239], [42509, 42511], - [42540, 42559], [42592, 42593], [42607, 42622], [42648, 42655], [42736, 42774], - [42784, 42785], [42889, 42890], [42893, 43002], [43043, 43055], [43062, 43071], - [43124, 43137], [43188, 43215], [43226, 43249], [43256, 43258], [43260, 43263], - [43302, 43311], [43335, 43359], [43389, 43395], [43443, 43470], [43482, 43519], - [43561, 43583], [43596, 43599], [43610, 43615], [43639, 43641], [43643, 43647], - [43698, 43700], [43703, 43704], [43710, 43711], [43715, 43738], [43742, 43967], - [44003, 44015], [44026, 44031], [55204, 55215], [55239, 55242], [55292, 55295], - [57344, 63743], [64046, 64047], [64110, 64111], [64218, 64255], [64263, 64274], - [64280, 64284], [64434, 64466], [64830, 64847], [64912, 64913], [64968, 65007], - [65020, 65135], [65277, 65295], [65306, 65312], [65339, 65344], [65371, 65381], - [65471, 65473], [65480, 65481], [65488, 65489], [65496, 65497]]; - for (i = 0; i < ranges.length; i++) { - start = ranges[i][0]; - end = ranges[i][1]; - for (j = start; j <= end; j++) { - result[j] = true; - } - } - return result; -})(); - -function splitQuery(query) { - var result = []; - var start = -1; - for (var i = 0; i < query.length; i++) { - if (splitChars[query.charCodeAt(i)]) { - if (start !== -1) { - result.push(query.slice(start, i)); - start = -1; - } - } else if (start === -1) { - start = i; - } - } - if 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Date: 2017-03-20T18:59Z - */ -( function( global, factory ) { - - "use strict"; - - if ( typeof module === "object" && typeof module.exports === "object" ) { - - // For CommonJS and CommonJS-like environments where a proper `window` - // is present, execute the factory and get jQuery. - // For environments that do not have a `window` with a `document` - // (such as Node.js), expose a factory as module.exports. - // This accentuates the need for the creation of a real `window`. - // e.g. var jQuery = require("jquery")(window); - // See ticket #14549 for more info. - module.exports = global.document ? - factory( global, true ) : - function( w ) { - if ( !w.document ) { - throw new Error( "jQuery requires a window with a document" ); - } - return factory( w ); - }; - } else { - factory( global ); - } - -// Pass this if window is not defined yet -} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) { - -// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1 -// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode -// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common -// enough that all such attempts are guarded in a try block. -"use strict"; - -var arr = []; - -var document = window.document; - -var getProto = Object.getPrototypeOf; - -var slice = arr.slice; - -var concat = arr.concat; - -var push = arr.push; - -var indexOf = arr.indexOf; - -var class2type = {}; - -var toString = class2type.toString; - -var hasOwn = class2type.hasOwnProperty; - -var fnToString = hasOwn.toString; - -var ObjectFunctionString = fnToString.call( Object ); - -var support = {}; - - - - function DOMEval( code, doc ) { - doc = doc || document; - - var script = doc.createElement( "script" ); - - script.text = code; - doc.head.appendChild( script ).parentNode.removeChild( script ); - } -/* global Symbol */ -// Defining this global in .eslintrc.json would create a danger of using the global -// unguarded in another place, it seems safer to define global only for this module - - - -var - version = "3.2.1", - - // Define a local copy of jQuery - jQuery = function( selector, context ) { - - // The jQuery object is actually just the init constructor 'enhanced' - // Need init if jQuery is called (just allow error to be thrown if not included) - return new jQuery.fn.init( selector, context ); - }, - - // Support: Android <=4.0 only - // Make sure we trim BOM and NBSP - rtrim = /^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g, - - // Matches dashed string for camelizing - rmsPrefix = /^-ms-/, - rdashAlpha = /-([a-z])/g, - - // Used by jQuery.camelCase as callback to replace() - fcamelCase = function( all, letter ) { - return letter.toUpperCase(); - }; - -jQuery.fn = jQuery.prototype = { - - // The current version of jQuery being used - jquery: version, - - constructor: jQuery, - - // The default length of a jQuery object is 0 - length: 0, - - toArray: function() { - return slice.call( this ); - }, - - // Get the Nth element in the matched element set OR - // Get the whole matched element set as a clean array - get: function( num ) { - - // Return all the elements in a clean array - if ( num == null ) { - return slice.call( this ); - } - - // Return just the one element from the set - return num < 0 ? this[ num + this.length ] : this[ num ]; - }, - - // Take an array of elements and push it onto the stack - // (returning the new matched element set) - pushStack: function( elems ) { - - // Build a new jQuery matched element set - var ret = jQuery.merge( this.constructor(), elems ); - - // Add the old object onto the stack (as a reference) - ret.prevObject = this; - - // Return the newly-formed element set - return ret; - }, - - // Execute a callback for every element in the matched set. - each: function( callback ) { - return jQuery.each( this, callback ); - }, - - map: function( callback ) { - return this.pushStack( jQuery.map( this, function( elem, i ) { - return callback.call( elem, i, elem ); - } ) ); - }, - - slice: function() { - return this.pushStack( slice.apply( this, arguments ) ); - }, - - first: function() { - return this.eq( 0 ); - }, - - last: function() { - return this.eq( -1 ); - }, - - eq: function( i ) { - var len = this.length, - j = +i + ( i < 0 ? len : 0 ); - return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] ); - }, - - end: function() { - return this.prevObject || this.constructor(); - }, - - // For internal use only. - // Behaves like an Array's method, not like a jQuery method. - push: push, - sort: arr.sort, - splice: arr.splice -}; - -jQuery.extend = jQuery.fn.extend = function() { - var options, name, src, copy, copyIsArray, clone, - target = arguments[ 0 ] || {}, - i = 1, - length = arguments.length, - deep = false; - - // Handle a deep copy situation - if ( typeof target === "boolean" ) { - deep = target; - - // Skip the boolean and the target - target = arguments[ i ] || {}; - i++; - } - - // Handle case when target is a string or something (possible in deep copy) - if ( typeof target !== "object" && !jQuery.isFunction( target ) ) { - target = {}; - } - - // Extend jQuery itself if only one argument is passed - if ( i === length ) { - target = this; - i--; - } - - for ( ; i < length; i++ ) { - - // Only deal with non-null/undefined values - if ( ( options = arguments[ i ] ) != null ) { - - // Extend the base object - for ( name in options ) { - src = target[ name ]; - copy = options[ name ]; - - // Prevent never-ending loop - if ( target === copy ) { - continue; - } - - // Recurse if we're merging plain objects or arrays - if ( deep && copy && ( jQuery.isPlainObject( copy ) || - ( copyIsArray = Array.isArray( copy ) ) ) ) { - - if ( copyIsArray ) { - copyIsArray = false; - clone = src && Array.isArray( src ) ? src : []; - - } else { - clone = src && jQuery.isPlainObject( src ) ? src : {}; - } - - // Never move original objects, clone them - target[ name ] = jQuery.extend( deep, clone, copy ); - - // Don't bring in undefined values - } else if ( copy !== undefined ) { - target[ name ] = copy; - } - } - } - } - - // Return the modified object - return target; -}; - -jQuery.extend( { - - // Unique for each copy of jQuery on the page - expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ), - - // Assume jQuery is ready without the ready module - isReady: true, - - error: function( msg ) { - throw new Error( msg ); - }, - - noop: function() {}, - - isFunction: function( obj ) { - return jQuery.type( obj ) === "function"; - }, - - isWindow: function( obj ) { - return obj != null && obj === obj.window; - }, - - isNumeric: function( obj ) { - - // As of jQuery 3.0, isNumeric is limited to - // strings and numbers (primitives or objects) - // that can be coerced to finite numbers (gh-2662) - var type = jQuery.type( obj ); - return ( type === "number" || type === "string" ) && - - // parseFloat NaNs numeric-cast false positives ("") - // ...but misinterprets leading-number strings, particularly hex literals ("0x...") - // subtraction forces infinities to NaN - !isNaN( obj - parseFloat( obj ) ); - }, - - isPlainObject: function( obj ) { - var proto, Ctor; - - // Detect obvious negatives - // Use toString instead of jQuery.type to catch host objects - if ( !obj || toString.call( obj ) !== "[object Object]" ) { - return false; - } - - proto = getProto( obj ); - - // Objects with no prototype (e.g., `Object.create( null )`) are plain - if ( !proto ) { - return true; - } - - // Objects with prototype are plain iff they were constructed by a global Object function - Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor; - return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString; - }, - - isEmptyObject: function( obj ) { - - /* eslint-disable no-unused-vars */ - // See https://github.com/eslint/eslint/issues/6125 - var name; - - for ( name in obj ) { - return false; - } - return true; - }, - - type: function( obj ) { - if ( obj == null ) { - return obj + ""; - } - - // Support: Android <=2.3 only (functionish RegExp) - return typeof obj === "object" || typeof obj === "function" ? - class2type[ toString.call( obj ) ] || "object" : - typeof obj; - }, - - // Evaluates a script in a global context - globalEval: function( code ) { - DOMEval( code ); - }, - - // Convert dashed to camelCase; used by the css and data modules - // Support: IE <=9 - 11, Edge 12 - 13 - // Microsoft forgot to hump their vendor prefix (#9572) - camelCase: function( string ) { - return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase ); - }, - - each: function( obj, callback ) { - var length, i = 0; - - if ( isArrayLike( obj ) ) { - length = obj.length; - for ( ; i < length; i++ ) { - if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { - break; - } - } - } else { - for ( i in obj ) { - if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { - break; - } - } - } - - return obj; - }, - - // Support: Android <=4.0 only - trim: function( text ) { - return text == null ? - "" : - ( text + "" ).replace( rtrim, "" ); - }, - - // results is for internal usage only - makeArray: function( arr, results ) { - var ret = results || []; - - if ( arr != null ) { - if ( isArrayLike( Object( arr ) ) ) { - jQuery.merge( ret, - typeof arr === "string" ? - [ arr ] : arr - ); - } else { - push.call( ret, arr ); - } - } - - return ret; - }, - - inArray: function( elem, arr, i ) { - return arr == null ? -1 : indexOf.call( arr, elem, i ); - }, - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - merge: function( first, second ) { - var len = +second.length, - j = 0, - i = first.length; - - for ( ; j < len; j++ ) { - first[ i++ ] = second[ j ]; - } - - first.length = i; - - return first; - }, - - grep: function( elems, callback, invert ) { - var callbackInverse, - matches = [], - i = 0, - length = elems.length, - callbackExpect = !invert; - - // Go through the array, only saving the items - // that pass the validator function - for ( ; i < length; i++ ) { - callbackInverse = !callback( elems[ i ], i ); - if ( callbackInverse !== callbackExpect ) { - matches.push( elems[ i ] ); - } - } - - return matches; - }, - - // arg is for internal usage only - map: function( elems, callback, arg ) { - var length, value, - i = 0, - ret = []; - - // Go through the array, translating each of the items to their new values - if ( isArrayLike( elems ) ) { - length = elems.length; - for ( ; i < length; i++ ) { - value = callback( elems[ i ], i, arg ); - - if ( value != null ) { - ret.push( value ); - } - } - - // Go through every key on the object, - } else { - for ( i in elems ) { - value = callback( elems[ i ], i, arg ); - - if ( value != null ) { - ret.push( value ); - } - } - } - - // Flatten any nested arrays - return concat.apply( [], ret ); - }, - - // A global GUID counter for objects - guid: 1, - - // Bind a function to a context, optionally partially applying any - // arguments. - proxy: function( fn, context ) { - var tmp, args, proxy; - - if ( typeof context === "string" ) { - tmp = fn[ context ]; - context = fn; - fn = tmp; - } - - // Quick check to determine if target is callable, in the spec - // this throws a TypeError, but we will just return undefined. - if ( !jQuery.isFunction( fn ) ) { - return undefined; - } - - // Simulated bind - args = slice.call( arguments, 2 ); - proxy = function() { - return fn.apply( context || this, args.concat( slice.call( arguments ) ) ); - }; - - // Set the guid of unique handler to the same of original handler, so it can be removed - proxy.guid = fn.guid = fn.guid || jQuery.guid++; - - return proxy; - }, - - now: Date.now, - - // jQuery.support is not used in Core but other projects attach their - // properties to it so it needs to exist. - support: support -} ); - -if ( typeof Symbol === "function" ) { - jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ]; -} - -// Populate the class2type map -jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ), -function( i, name ) { - class2type[ "[object " + name + "]" ] = name.toLowerCase(); -} ); - -function isArrayLike( obj ) { - - // Support: real iOS 8.2 only (not reproducible in simulator) - // `in` check used to prevent JIT error (gh-2145) - // hasOwn isn't used here due to false negatives - // regarding Nodelist length in IE - var length = !!obj && "length" in obj && obj.length, - type = jQuery.type( obj ); - - if ( type === "function" || jQuery.isWindow( obj ) ) { - return false; - } - - return type === "array" || length === 0 || - typeof length === "number" && length > 0 && ( length - 1 ) in obj; -} -var Sizzle = -/*! - * Sizzle CSS Selector Engine v2.3.3 - * https://sizzlejs.com/ - * - * Copyright jQuery Foundation and other contributors - * Released under the MIT license - * http://jquery.org/license - * - * Date: 2016-08-08 - */ -(function( window ) { - -var i, - support, - Expr, - getText, - isXML, - tokenize, - compile, - select, - outermostContext, - sortInput, - hasDuplicate, - - // Local document vars - setDocument, - document, - docElem, - documentIsHTML, - rbuggyQSA, - rbuggyMatches, - matches, - contains, - - // Instance-specific data - expando = "sizzle" + 1 * new Date(), - preferredDoc = window.document, - dirruns = 0, - done = 0, - classCache = createCache(), - tokenCache = createCache(), - compilerCache = createCache(), - sortOrder = function( a, b ) { - if ( a === b ) { - hasDuplicate = true; - } - return 0; - }, - - // Instance methods - hasOwn = ({}).hasOwnProperty, - arr = [], - pop = arr.pop, - push_native = arr.push, - push = arr.push, - slice = arr.slice, - // Use a stripped-down indexOf as it's faster than native - // https://jsperf.com/thor-indexof-vs-for/5 - indexOf = function( list, elem ) { - var i = 0, - len = list.length; - for ( ; i < len; i++ ) { - if ( list[i] === elem ) { - return i; - } - } - return -1; - }, - - booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|ismap|loop|multiple|open|readonly|required|scoped", - - // Regular expressions - - // http://www.w3.org/TR/css3-selectors/#whitespace - whitespace = "[\\x20\\t\\r\\n\\f]", - - // http://www.w3.org/TR/CSS21/syndata.html#value-def-identifier - identifier = "(?:\\\\.|[\\w-]|[^\0-\\xa0])+", - - // Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors - attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace + - // Operator (capture 2) - "*([*^$|!~]?=)" + whitespace + - // "Attribute values must be CSS identifiers [capture 5] or strings [capture 3 or capture 4]" - "*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" + whitespace + - "*\\]", - - pseudos = ":(" + identifier + ")(?:\\((" + - // To reduce the number of selectors needing tokenize in the preFilter, prefer arguments: - // 1. quoted (capture 3; capture 4 or capture 5) - "('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" + - // 2. simple (capture 6) - "((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" + - // 3. anything else (capture 2) - ".*" + - ")\\)|)", - - // Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter - rwhitespace = new RegExp( whitespace + "+", "g" ), - rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" + whitespace + "+$", "g" ), - - rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ), - rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace + "*" ), - - rattributeQuotes = new RegExp( "=" + whitespace + "*([^\\]'\"]*?)" + whitespace + "*\\]", "g" ), - - rpseudo = new RegExp( pseudos ), - ridentifier = new RegExp( "^" + identifier + "$" ), - - matchExpr = { - "ID": new RegExp( "^#(" + identifier + ")" ), - "CLASS": new RegExp( "^\\.(" + identifier + ")" ), - "TAG": new RegExp( "^(" + identifier + "|[*])" ), - "ATTR": new RegExp( "^" + attributes ), - "PSEUDO": new RegExp( "^" + pseudos ), - "CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" + whitespace + - "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" + whitespace + - "*(\\d+)|))" + whitespace + "*\\)|)", "i" ), - "bool": new RegExp( "^(?:" + booleans + ")$", "i" ), - // For use in libraries implementing .is() - // We use this for POS matching in `select` - "needsContext": new RegExp( "^" + whitespace + "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + - whitespace + "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" ) - }, - - rinputs = /^(?:input|select|textarea|button)$/i, - rheader = /^h\d$/i, - - rnative = /^[^{]+\{\s*\[native \w/, - - // Easily-parseable/retrievable ID or TAG or CLASS selectors - rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/, - - rsibling = /[+~]/, - - // CSS escapes - // http://www.w3.org/TR/CSS21/syndata.html#escaped-characters - runescape = new RegExp( "\\\\([\\da-f]{1,6}" + whitespace + "?|(" + whitespace + ")|.)", "ig" ), - funescape = function( _, escaped, escapedWhitespace ) { - var high = "0x" + escaped - 0x10000; - // NaN means non-codepoint - // Support: Firefox<24 - // Workaround erroneous numeric interpretation of +"0x" - return high !== high || escapedWhitespace ? - escaped : - high < 0 ? - // BMP codepoint - String.fromCharCode( high + 0x10000 ) : - // Supplemental Plane codepoint (surrogate pair) - String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 ); - }, - - // CSS string/identifier serialization - // https://drafts.csswg.org/cssom/#common-serializing-idioms - rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g, - fcssescape = function( ch, asCodePoint ) { - if ( asCodePoint ) { - - // U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER - if ( ch === "\0" ) { - return "\uFFFD"; - } - - // Control characters and (dependent upon position) numbers get escaped as code points - return ch.slice( 0, -1 ) + "\\" + ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " "; - } - - // Other potentially-special ASCII characters get backslash-escaped - return "\\" + ch; - }, - - // Used for iframes - // See setDocument() - // Removing the function wrapper causes a "Permission Denied" - // error in IE - unloadHandler = function() { - setDocument(); - }, - - disabledAncestor = addCombinator( - function( elem ) { - return elem.disabled === true && ("form" in elem || "label" in elem); - }, - { dir: "parentNode", next: "legend" } - ); - -// Optimize for push.apply( _, NodeList ) -try { - push.apply( - (arr = slice.call( preferredDoc.childNodes )), - preferredDoc.childNodes - ); - // Support: Android<4.0 - // Detect silently failing push.apply - arr[ preferredDoc.childNodes.length ].nodeType; -} catch ( e ) { - push = { apply: arr.length ? - - // Leverage slice if possible - function( target, els ) { - push_native.apply( target, slice.call(els) ); - } : - - // Support: IE<9 - // Otherwise append directly - function( target, els ) { - var j = target.length, - i = 0; - // Can't trust NodeList.length - while ( (target[j++] = els[i++]) ) {} - target.length = j - 1; - } - }; -} - -function Sizzle( selector, context, results, seed ) { - var m, i, elem, nid, match, groups, newSelector, - newContext = context && context.ownerDocument, - - // nodeType defaults to 9, since context defaults to document - nodeType = context ? context.nodeType : 9; - - results = results || []; - - // Return early from calls with invalid selector or context - if ( typeof selector !== "string" || !selector || - nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) { - - return results; - } - - // Try to shortcut find operations (as opposed to filters) in HTML documents - if ( !seed ) { - - if ( ( context ? context.ownerDocument || context : preferredDoc ) !== document ) { - setDocument( context ); - } - context = context || document; - - if ( documentIsHTML ) { - - // If the selector is sufficiently simple, try using a "get*By*" DOM method - // (excepting DocumentFragment context, where the methods don't exist) - if ( nodeType !== 11 && (match = rquickExpr.exec( selector )) ) { - - // ID selector - if ( (m = match[1]) ) { - - // Document context - if ( nodeType === 9 ) { - if ( (elem = context.getElementById( m )) ) { - - // Support: IE, Opera, Webkit - // TODO: identify versions - // getElementById can match elements by name instead of ID - if ( elem.id === m ) { - results.push( elem ); - return results; - } - } else { - return results; - } - - // Element context - } else { - - // Support: IE, Opera, Webkit - // TODO: identify versions - // getElementById can match elements by name instead of ID - if ( newContext && (elem = newContext.getElementById( m )) && - contains( context, elem ) && - elem.id === m ) { - - results.push( elem ); - return results; - } - } - - // Type selector - } else if ( match[2] ) { - push.apply( results, context.getElementsByTagName( selector ) ); - return results; - - // Class selector - } else if ( (m = match[3]) && support.getElementsByClassName && - context.getElementsByClassName ) { - - push.apply( results, context.getElementsByClassName( m ) ); - return results; - } - } - - // Take advantage of querySelectorAll - if ( support.qsa && - !compilerCache[ selector + " " ] && - (!rbuggyQSA || !rbuggyQSA.test( selector )) ) { - - if ( nodeType !== 1 ) { - newContext = context; - newSelector = selector; - - // qSA looks outside Element context, which is not what we want - // Thanks to Andrew Dupont for this workaround technique - // Support: IE <=8 - // Exclude object elements - } else if ( context.nodeName.toLowerCase() !== "object" ) { - - // Capture the context ID, setting it first if necessary - if ( (nid = context.getAttribute( "id" )) ) { - nid = nid.replace( rcssescape, fcssescape ); - } else { - context.setAttribute( "id", (nid = expando) ); - } - - // Prefix every selector in the list - groups = tokenize( selector ); - i = groups.length; - while ( i-- ) { - groups[i] = "#" + nid + " " + toSelector( groups[i] ); - } - newSelector = groups.join( "," ); - - // Expand context for sibling selectors - newContext = rsibling.test( selector ) && testContext( context.parentNode ) || - context; - } - - if ( newSelector ) { - try { - push.apply( results, - newContext.querySelectorAll( newSelector ) - ); - return results; - } catch ( qsaError ) { - } finally { - if ( nid === expando ) { - context.removeAttribute( "id" ); - } - } - } - } - } - } - - // All others - return select( selector.replace( rtrim, "$1" ), context, results, seed ); -} - -/** - * Create key-value caches of limited size - * @returns {function(string, object)} Returns the Object data after storing it on itself with - * property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength) - * deleting the oldest entry - */ -function createCache() { - var keys = []; - - function cache( key, value ) { - // Use (key + " ") to avoid collision with native prototype properties (see Issue #157) - if ( keys.push( key + " " ) > Expr.cacheLength ) { - // Only keep the most recent entries - delete cache[ keys.shift() ]; - } - return (cache[ key + " " ] = value); - } - return cache; -} - -/** - * Mark a function for special use by Sizzle - * @param {Function} fn The function to mark - */ -function markFunction( fn ) { - fn[ expando ] = true; - return fn; -} - -/** - * Support testing using an element - * @param {Function} fn Passed the created element and returns a boolean result - */ -function assert( fn ) { - var el = document.createElement("fieldset"); - - try { - return !!fn( el ); - } catch (e) { - return false; - } finally { - // Remove from its parent by default - if ( el.parentNode ) { - el.parentNode.removeChild( el ); - } - // release memory in IE - el = null; - } -} - -/** - * Adds the same handler for all of the specified attrs - * @param {String} attrs Pipe-separated list of attributes - * @param {Function} handler The method that will be applied - */ -function addHandle( attrs, handler ) { - var arr = attrs.split("|"), - i = arr.length; - - while ( i-- ) { - Expr.attrHandle[ arr[i] ] = handler; - } -} - -/** - * Checks document order of two siblings - * @param {Element} a - * @param {Element} b - * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b - */ -function siblingCheck( a, b ) { - var cur = b && a, - diff = cur && a.nodeType === 1 && b.nodeType === 1 && - a.sourceIndex - b.sourceIndex; - - // Use IE sourceIndex if available on both nodes - if ( diff ) { - return diff; - } - - // Check if b follows a - if ( cur ) { - while ( (cur = cur.nextSibling) ) { - if ( cur === b ) { - return -1; - } - } - } - - return a ? 1 : -1; -} - -/** - * Returns a function to use in pseudos for input types - * @param {String} type - */ -function createInputPseudo( type ) { - return function( elem ) { - var name = elem.nodeName.toLowerCase(); - return name === "input" && elem.type === type; - }; -} - -/** - * Returns a function to use in pseudos for buttons - * @param {String} type - */ -function createButtonPseudo( type ) { - return function( elem ) { - var name = elem.nodeName.toLowerCase(); - return (name === "input" || name === "button") && elem.type === type; - }; -} - -/** - * Returns a function to use in pseudos for :enabled/:disabled - * @param {Boolean} disabled true for :disabled; false for :enabled - */ -function createDisabledPseudo( disabled ) { - - // Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable - return function( elem ) { - - // Only certain elements can match :enabled or :disabled - // https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled - // https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled - if ( "form" in elem ) { - - // Check for inherited disabledness on relevant non-disabled elements: - // * listed form-associated elements in a disabled fieldset - // https://html.spec.whatwg.org/multipage/forms.html#category-listed - // https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled - // * option elements in a disabled optgroup - // https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled - // All such elements have a "form" property. - if ( elem.parentNode && elem.disabled === false ) { - - // Option elements defer to a parent optgroup if present - if ( "label" in elem ) { - if ( "label" in elem.parentNode ) { - return elem.parentNode.disabled === disabled; - } else { - return elem.disabled === disabled; - } - } - - // Support: IE 6 - 11 - // Use the isDisabled shortcut property to check for disabled fieldset ancestors - return elem.isDisabled === disabled || - - // Where there is no isDisabled, check manually - /* jshint -W018 */ - elem.isDisabled !== !disabled && - disabledAncestor( elem ) === disabled; - } - - return elem.disabled === disabled; - - // Try to winnow out elements that can't be disabled before trusting the disabled property. - // Some victims get caught in our net (label, legend, menu, track), but it shouldn't - // even exist on them, let alone have a boolean value. - } else if ( "label" in elem ) { - return elem.disabled === disabled; - } - - // Remaining elements are neither :enabled nor :disabled - return false; - }; -} - -/** - * Returns a function to use in pseudos for positionals - * @param {Function} fn - */ -function createPositionalPseudo( fn ) { - return markFunction(function( argument ) { - argument = +argument; - return markFunction(function( seed, matches ) { - var j, - matchIndexes = fn( [], seed.length, argument ), - i = matchIndexes.length; - - // Match elements found at the specified indexes - while ( i-- ) { - if ( seed[ (j = matchIndexes[i]) ] ) { - seed[j] = !(matches[j] = seed[j]); - } - } - }); - }); -} - -/** - * Checks a node for validity as a Sizzle context - * @param {Element|Object=} context - * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value - */ -function testContext( context ) { - return context && typeof context.getElementsByTagName !== "undefined" && context; -} - -// Expose support vars for convenience -support = Sizzle.support = {}; - -/** - * Detects XML nodes - * @param {Element|Object} elem An element or a document - * @returns {Boolean} True iff elem is a non-HTML XML node - */ -isXML = Sizzle.isXML = function( elem ) { - // documentElement is verified for cases where it doesn't yet exist - // (such as loading iframes in IE - #4833) - var documentElement = elem && (elem.ownerDocument || elem).documentElement; - return documentElement ? documentElement.nodeName !== "HTML" : false; -}; - -/** - * Sets document-related variables once based on the current document - * @param {Element|Object} [doc] An element or document object to use to set the document - * @returns {Object} Returns the current document - */ -setDocument = Sizzle.setDocument = function( node ) { - var hasCompare, subWindow, - doc = node ? node.ownerDocument || node : preferredDoc; - - // Return early if doc is invalid or already selected - if ( doc === document || doc.nodeType !== 9 || !doc.documentElement ) { - return document; - } - - // Update global variables - document = doc; - docElem = document.documentElement; - documentIsHTML = !isXML( document ); - - // Support: IE 9-11, Edge - // Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936) - if ( preferredDoc !== document && - (subWindow = document.defaultView) && subWindow.top !== subWindow ) { - - // Support: IE 11, Edge - if ( subWindow.addEventListener ) { - subWindow.addEventListener( "unload", unloadHandler, false ); - - // Support: IE 9 - 10 only - } else if ( subWindow.attachEvent ) { - subWindow.attachEvent( "onunload", unloadHandler ); - } - } - - /* Attributes - ---------------------------------------------------------------------- */ - - // Support: IE<8 - // Verify that getAttribute really returns attributes and not properties - // (excepting IE8 booleans) - support.attributes = assert(function( el ) { - el.className = "i"; - return !el.getAttribute("className"); - }); - - /* getElement(s)By* - ---------------------------------------------------------------------- */ - - // Check if getElementsByTagName("*") returns only elements - support.getElementsByTagName = assert(function( el ) { - el.appendChild( document.createComment("") ); - return !el.getElementsByTagName("*").length; - }); - - // Support: IE<9 - support.getElementsByClassName = rnative.test( document.getElementsByClassName ); - - // Support: IE<10 - // Check if getElementById returns elements by name - // The broken getElementById methods don't pick up programmatically-set names, - // so use a roundabout getElementsByName test - support.getById = assert(function( el ) { - docElem.appendChild( el ).id = expando; - return !document.getElementsByName || !document.getElementsByName( expando ).length; - }); - - // ID filter and find - if ( support.getById ) { - Expr.filter["ID"] = function( id ) { - var attrId = id.replace( runescape, funescape ); - return function( elem ) { - return elem.getAttribute("id") === attrId; - }; - }; - Expr.find["ID"] = function( id, context ) { - if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { - var elem = context.getElementById( id ); - return elem ? [ elem ] : []; - } - }; - } else { - Expr.filter["ID"] = function( id ) { - var attrId = id.replace( runescape, funescape ); - return function( elem ) { - var node = typeof elem.getAttributeNode !== "undefined" && - elem.getAttributeNode("id"); - return node && node.value === attrId; - }; - }; - - // Support: IE 6 - 7 only - // getElementById is not reliable as a find shortcut - Expr.find["ID"] = function( id, context ) { - if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { - var node, i, elems, - elem = context.getElementById( id ); - - if ( elem ) { - - // Verify the id attribute - node = elem.getAttributeNode("id"); - if ( node && node.value === id ) { - return [ elem ]; - } - - // Fall back on getElementsByName - elems = context.getElementsByName( id ); - i = 0; - while ( (elem = elems[i++]) ) { - node = elem.getAttributeNode("id"); - if ( node && node.value === id ) { - return [ elem ]; - } - } - } - - return []; - } - }; - } - - // Tag - Expr.find["TAG"] = support.getElementsByTagName ? - function( tag, context ) { - if ( typeof context.getElementsByTagName !== "undefined" ) { - return context.getElementsByTagName( tag ); - - // DocumentFragment nodes don't have gEBTN - } else if ( support.qsa ) { - return context.querySelectorAll( tag ); - } - } : - - function( tag, context ) { - var elem, - tmp = [], - i = 0, - // By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too - results = context.getElementsByTagName( tag ); - - // Filter out possible comments - if ( tag === "*" ) { - while ( (elem = results[i++]) ) { - if ( elem.nodeType === 1 ) { - tmp.push( elem ); - } - } - - return tmp; - } - return results; - }; - - // Class - Expr.find["CLASS"] = support.getElementsByClassName && function( className, context ) { - if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) { - return context.getElementsByClassName( className ); - } - }; - - /* QSA/matchesSelector - ---------------------------------------------------------------------- */ - - // QSA and matchesSelector support - - // matchesSelector(:active) reports false when true (IE9/Opera 11.5) - rbuggyMatches = []; - - // qSa(:focus) reports false when true (Chrome 21) - // We allow this because of a bug in IE8/9 that throws an error - // whenever `document.activeElement` is accessed on an iframe - // So, we allow :focus to pass through QSA all the time to avoid the IE error - // See https://bugs.jquery.com/ticket/13378 - rbuggyQSA = []; - - if ( (support.qsa = rnative.test( document.querySelectorAll )) ) { - // Build QSA regex - // Regex strategy adopted from Diego Perini - assert(function( el ) { - // Select is set to empty string on purpose - // This is to test IE's treatment of not explicitly - // setting a boolean content attribute, - // since its presence should be enough - // https://bugs.jquery.com/ticket/12359 - docElem.appendChild( el ).innerHTML = "" + - ""; - - // Support: IE8, Opera 11-12.16 - // Nothing should be selected when empty strings follow ^= or $= or *= - // The test attribute must be unknown in Opera but "safe" for WinRT - // https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section - if ( el.querySelectorAll("[msallowcapture^='']").length ) { - rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" ); - } - - // Support: IE8 - // Boolean attributes and "value" are not treated correctly - if ( !el.querySelectorAll("[selected]").length ) { - rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" ); - } - - // Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+ - if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) { - rbuggyQSA.push("~="); - } - - // Webkit/Opera - :checked should return selected option elements - // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked - // IE8 throws error here and will not see later tests - if ( !el.querySelectorAll(":checked").length ) { - rbuggyQSA.push(":checked"); - } - - // Support: Safari 8+, iOS 8+ - // https://bugs.webkit.org/show_bug.cgi?id=136851 - // In-page `selector#id sibling-combinator selector` fails - if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) { - rbuggyQSA.push(".#.+[+~]"); - } - }); - - assert(function( el ) { - el.innerHTML = "" + - ""; - - // Support: Windows 8 Native Apps - // The type and name attributes are restricted during .innerHTML assignment - var input = document.createElement("input"); - input.setAttribute( "type", "hidden" ); - el.appendChild( input ).setAttribute( "name", "D" ); - - // Support: IE8 - // Enforce case-sensitivity of name attribute - if ( el.querySelectorAll("[name=d]").length ) { - rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" ); - } - - // FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled) - // IE8 throws error here and will not see later tests - if ( el.querySelectorAll(":enabled").length !== 2 ) { - rbuggyQSA.push( ":enabled", ":disabled" ); - } - - // Support: IE9-11+ - // IE's :disabled selector does not pick up the children of disabled fieldsets - docElem.appendChild( el ).disabled = true; - if ( el.querySelectorAll(":disabled").length !== 2 ) { - rbuggyQSA.push( ":enabled", ":disabled" ); - } - - // Opera 10-11 does not throw on post-comma invalid pseudos - el.querySelectorAll("*,:x"); - rbuggyQSA.push(",.*:"); - }); - } - - if ( (support.matchesSelector = rnative.test( (matches = docElem.matches || - docElem.webkitMatchesSelector || - docElem.mozMatchesSelector || - docElem.oMatchesSelector || - docElem.msMatchesSelector) )) ) { - - assert(function( el ) { - // Check to see if it's possible to do matchesSelector - // on a disconnected node (IE 9) - support.disconnectedMatch = matches.call( el, "*" ); - - // This should fail with an exception - // Gecko does not error, returns false instead - matches.call( el, "[s!='']:x" ); - rbuggyMatches.push( "!=", pseudos ); - }); - } - - rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join("|") ); - rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join("|") ); - - /* Contains - ---------------------------------------------------------------------- */ - hasCompare = rnative.test( docElem.compareDocumentPosition ); - - // Element contains another - // Purposefully self-exclusive - // As in, an element does not contain itself - contains = hasCompare || rnative.test( docElem.contains ) ? - function( a, b ) { - var adown = a.nodeType === 9 ? a.documentElement : a, - bup = b && b.parentNode; - return a === bup || !!( bup && bup.nodeType === 1 && ( - adown.contains ? - adown.contains( bup ) : - a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16 - )); - } : - function( a, b ) { - if ( b ) { - while ( (b = b.parentNode) ) { - if ( b === a ) { - return true; - } - } - } - return false; - }; - - /* Sorting - ---------------------------------------------------------------------- */ - - // Document order sorting - sortOrder = hasCompare ? - function( a, b ) { - - // Flag for duplicate removal - if ( a === b ) { - hasDuplicate = true; - return 0; - } - - // Sort on method existence if only one input has compareDocumentPosition - var compare = !a.compareDocumentPosition - !b.compareDocumentPosition; - if ( compare ) { - return compare; - } - - // Calculate position if both inputs belong to the same document - compare = ( a.ownerDocument || a ) === ( b.ownerDocument || b ) ? - a.compareDocumentPosition( b ) : - - // Otherwise we know they are disconnected - 1; - - // Disconnected nodes - if ( compare & 1 || - (!support.sortDetached && b.compareDocumentPosition( a ) === compare) ) { - - // Choose the first element that is related to our preferred document - if ( a === document || a.ownerDocument === preferredDoc && contains(preferredDoc, a) ) { - return -1; - } - if ( b === document || b.ownerDocument === preferredDoc && contains(preferredDoc, b) ) { - return 1; - } - - // Maintain original order - return sortInput ? - ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : - 0; - } - - return compare & 4 ? -1 : 1; - } : - function( a, b ) { - // Exit early if the nodes are identical - if ( a === b ) { - hasDuplicate = true; - return 0; - } - - var cur, - i = 0, - aup = a.parentNode, - bup = b.parentNode, - ap = [ a ], - bp = [ b ]; - - // Parentless nodes are either documents or disconnected - if ( !aup || !bup ) { - return a === document ? -1 : - b === document ? 1 : - aup ? -1 : - bup ? 1 : - sortInput ? - ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : - 0; - - // If the nodes are siblings, we can do a quick check - } else if ( aup === bup ) { - return siblingCheck( a, b ); - } - - // Otherwise we need full lists of their ancestors for comparison - cur = a; - while ( (cur = cur.parentNode) ) { - ap.unshift( cur ); - } - cur = b; - while ( (cur = cur.parentNode) ) { - bp.unshift( cur ); - } - - // Walk down the tree looking for a discrepancy - while ( ap[i] === bp[i] ) { - i++; - } - - return i ? - // Do a sibling check if the nodes have a common ancestor - siblingCheck( ap[i], bp[i] ) : - - // Otherwise nodes in our document sort first - ap[i] === preferredDoc ? -1 : - bp[i] === preferredDoc ? 1 : - 0; - }; - - return document; -}; - -Sizzle.matches = function( expr, elements ) { - return Sizzle( expr, null, null, elements ); -}; - -Sizzle.matchesSelector = function( elem, expr ) { - // Set document vars if needed - if ( ( elem.ownerDocument || elem ) !== document ) { - setDocument( elem ); - } - - // Make sure that attribute selectors are quoted - expr = expr.replace( rattributeQuotes, "='$1']" ); - - if ( support.matchesSelector && documentIsHTML && - !compilerCache[ expr + " " ] && - ( !rbuggyMatches || !rbuggyMatches.test( expr ) ) && - ( !rbuggyQSA || !rbuggyQSA.test( expr ) ) ) { - - try { - var ret = matches.call( elem, expr ); - - // IE 9's matchesSelector returns false on disconnected nodes - if ( ret || support.disconnectedMatch || - // As well, disconnected nodes are said to be in a document - // fragment in IE 9 - elem.document && elem.document.nodeType !== 11 ) { - return ret; - } - } catch (e) {} - } - - return Sizzle( expr, document, null, [ elem ] ).length > 0; -}; - -Sizzle.contains = function( context, elem ) { - // Set document vars if needed - if ( ( context.ownerDocument || context ) !== document ) { - setDocument( context ); - } - return contains( context, elem ); -}; - -Sizzle.attr = function( elem, name ) { - // Set document vars if needed - if ( ( elem.ownerDocument || elem ) !== document ) { - setDocument( elem ); - } - - var fn = Expr.attrHandle[ name.toLowerCase() ], - // Don't get fooled by Object.prototype properties (jQuery #13807) - val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ? - fn( elem, name, !documentIsHTML ) : - undefined; - - return val !== undefined ? - val : - support.attributes || !documentIsHTML ? - elem.getAttribute( name ) : - (val = elem.getAttributeNode(name)) && val.specified ? - val.value : - null; -}; - -Sizzle.escape = function( sel ) { - return (sel + "").replace( rcssescape, fcssescape ); -}; - -Sizzle.error = function( msg ) { - throw new Error( "Syntax error, unrecognized expression: " + msg ); -}; - -/** - * Document sorting and removing duplicates - * @param {ArrayLike} results - */ -Sizzle.uniqueSort = function( results ) { - var elem, - duplicates = [], - j = 0, - i = 0; - - // Unless we *know* we can detect duplicates, assume their presence - hasDuplicate = !support.detectDuplicates; - sortInput = !support.sortStable && results.slice( 0 ); - results.sort( sortOrder ); - - if ( hasDuplicate ) { - while ( (elem = results[i++]) ) { - if ( elem === results[ i ] ) { - j = duplicates.push( i ); - } - } - while ( j-- ) { - results.splice( duplicates[ j ], 1 ); - } - } - - // Clear input after sorting to release objects - // See https://github.com/jquery/sizzle/pull/225 - sortInput = null; - - return results; -}; - -/** - * Utility function for retrieving the text value of an array of DOM nodes - * @param {Array|Element} elem - */ -getText = Sizzle.getText = function( elem ) { - var node, - ret = "", - i = 0, - nodeType = elem.nodeType; - - if ( !nodeType ) { - // If no nodeType, this is expected to be an array - while ( (node = elem[i++]) ) { - // Do not traverse comment nodes - ret += getText( node ); - } - } else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) { - // Use textContent for elements - // innerText usage removed for consistency of new lines (jQuery #11153) - if ( typeof elem.textContent === "string" ) { - return elem.textContent; - } else { - // Traverse its children - for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { - ret += getText( elem ); - } - } - } else if ( nodeType === 3 || nodeType === 4 ) { - return elem.nodeValue; - } - // Do not include comment or processing instruction nodes - - return ret; -}; - -Expr = Sizzle.selectors = { - - // Can be adjusted by the user - cacheLength: 50, - - createPseudo: markFunction, - - match: matchExpr, - - attrHandle: {}, - - find: {}, - - relative: { - ">": { dir: "parentNode", first: true }, - " ": { dir: "parentNode" }, - "+": { dir: "previousSibling", first: true }, - "~": { dir: "previousSibling" } - }, - - preFilter: { - "ATTR": function( match ) { - match[1] = match[1].replace( runescape, funescape ); - - // Move the given value to match[3] whether quoted or unquoted - match[3] = ( match[3] || match[4] || match[5] || "" ).replace( runescape, funescape ); - - if ( match[2] === "~=" ) { - match[3] = " " + match[3] + " "; - } - - return match.slice( 0, 4 ); - }, - - "CHILD": function( match ) { - /* matches from matchExpr["CHILD"] - 1 type (only|nth|...) - 2 what (child|of-type) - 3 argument (even|odd|\d*|\d*n([+-]\d+)?|...) - 4 xn-component of xn+y argument ([+-]?\d*n|) - 5 sign of xn-component - 6 x of xn-component - 7 sign of y-component - 8 y of y-component - */ - match[1] = match[1].toLowerCase(); - - if ( match[1].slice( 0, 3 ) === "nth" ) { - // nth-* requires argument - if ( !match[3] ) { - Sizzle.error( match[0] ); - } - - // numeric x and y parameters for Expr.filter.CHILD - // remember that false/true cast respectively to 0/1 - match[4] = +( match[4] ? match[5] + (match[6] || 1) : 2 * ( match[3] === "even" || match[3] === "odd" ) ); - match[5] = +( ( match[7] + match[8] ) || match[3] === "odd" ); - - // other types prohibit arguments - } else if ( match[3] ) { - Sizzle.error( match[0] ); - } - - return match; - }, - - "PSEUDO": function( match ) { - var excess, - unquoted = !match[6] && match[2]; - - if ( matchExpr["CHILD"].test( match[0] ) ) { - return null; - } - - // Accept quoted arguments as-is - if ( match[3] ) { - match[2] = match[4] || match[5] || ""; - - // Strip excess characters from unquoted arguments - } else if ( unquoted && rpseudo.test( unquoted ) && - // Get excess from tokenize (recursively) - (excess = tokenize( unquoted, true )) && - // advance to the next closing parenthesis - (excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length) ) { - - // excess is a negative index - match[0] = match[0].slice( 0, excess ); - match[2] = unquoted.slice( 0, excess ); - } - - // Return only captures needed by the pseudo filter method (type and argument) - return match.slice( 0, 3 ); - } - }, - - filter: { - - "TAG": function( nodeNameSelector ) { - var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase(); - return nodeNameSelector === "*" ? - function() { return true; } : - function( elem ) { - return elem.nodeName && elem.nodeName.toLowerCase() === nodeName; - }; - }, - - "CLASS": function( className ) { - var pattern = classCache[ className + " " ]; - - return pattern || - (pattern = new RegExp( "(^|" + whitespace + ")" + className + "(" + whitespace + "|$)" )) && - classCache( className, function( elem ) { - return pattern.test( typeof elem.className === "string" && elem.className || typeof elem.getAttribute !== "undefined" && elem.getAttribute("class") || "" ); - }); - }, - - "ATTR": function( name, operator, check ) { - return function( elem ) { - var result = Sizzle.attr( elem, name ); - - if ( result == null ) { - return operator === "!="; - } - if ( !operator ) { - return true; - } - - result += ""; - - return operator === "=" ? result === check : - operator === "!=" ? result !== check : - operator === "^=" ? check && result.indexOf( check ) === 0 : - operator === "*=" ? check && result.indexOf( check ) > -1 : - operator === "$=" ? check && result.slice( -check.length ) === check : - operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 : - operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" : - false; - }; - }, - - "CHILD": function( type, what, argument, first, last ) { - var simple = type.slice( 0, 3 ) !== "nth", - forward = type.slice( -4 ) !== "last", - ofType = what === "of-type"; - - return first === 1 && last === 0 ? - - // Shortcut for :nth-*(n) - function( elem ) { - return !!elem.parentNode; - } : - - function( elem, context, xml ) { - var cache, uniqueCache, outerCache, node, nodeIndex, start, - dir = simple !== forward ? "nextSibling" : "previousSibling", - parent = elem.parentNode, - name = ofType && elem.nodeName.toLowerCase(), - useCache = !xml && !ofType, - diff = false; - - if ( parent ) { - - // :(first|last|only)-(child|of-type) - if ( simple ) { - while ( dir ) { - node = elem; - while ( (node = node[ dir ]) ) { - if ( ofType ? - node.nodeName.toLowerCase() === name : - node.nodeType === 1 ) { - - return false; - } - } - // Reverse direction for :only-* (if we haven't yet done so) - start = dir = type === "only" && !start && "nextSibling"; - } - return true; - } - - start = [ forward ? parent.firstChild : parent.lastChild ]; - - // non-xml :nth-child(...) stores cache data on `parent` - if ( forward && useCache ) { - - // Seek `elem` from a previously-cached index - - // ...in a gzip-friendly way - node = parent; - outerCache = node[ expando ] || (node[ expando ] = {}); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - (outerCache[ node.uniqueID ] = {}); - - cache = uniqueCache[ type ] || []; - nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; - diff = nodeIndex && cache[ 2 ]; - node = nodeIndex && parent.childNodes[ nodeIndex ]; - - while ( (node = ++nodeIndex && node && node[ dir ] || - - // Fallback to seeking `elem` from the start - (diff = nodeIndex = 0) || start.pop()) ) { - - // When found, cache indexes on `parent` and break - if ( node.nodeType === 1 && ++diff && node === elem ) { - uniqueCache[ type ] = [ dirruns, nodeIndex, diff ]; - break; - } - } - - } else { - // Use previously-cached element index if available - if ( useCache ) { - // ...in a gzip-friendly way - node = elem; - outerCache = node[ expando ] || (node[ expando ] = {}); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - (outerCache[ node.uniqueID ] = {}); - - cache = uniqueCache[ type ] || []; - nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; - diff = nodeIndex; - } - - // xml :nth-child(...) - // or :nth-last-child(...) or :nth(-last)?-of-type(...) - if ( diff === false ) { - // Use the same loop as above to seek `elem` from the start - while ( (node = ++nodeIndex && node && node[ dir ] || - (diff = nodeIndex = 0) || start.pop()) ) { - - if ( ( ofType ? - node.nodeName.toLowerCase() === name : - node.nodeType === 1 ) && - ++diff ) { - - // Cache the index of each encountered element - if ( useCache ) { - outerCache = node[ expando ] || (node[ expando ] = {}); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - (outerCache[ node.uniqueID ] = {}); - - uniqueCache[ type ] = [ dirruns, diff ]; - } - - if ( node === elem ) { - break; - } - } - } - } - } - - // Incorporate the offset, then check against cycle size - diff -= last; - return diff === first || ( diff % first === 0 && diff / first >= 0 ); - } - }; - }, - - "PSEUDO": function( pseudo, argument ) { - // pseudo-class names are case-insensitive - // http://www.w3.org/TR/selectors/#pseudo-classes - // Prioritize by case sensitivity in case custom pseudos are added with uppercase letters - // Remember that setFilters inherits from pseudos - var args, - fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] || - Sizzle.error( "unsupported pseudo: " + pseudo ); - - // The user may use createPseudo to indicate that - // arguments are needed to create the filter function - // just as Sizzle does - if ( fn[ expando ] ) { - return fn( argument ); - } - - // But maintain support for old signatures - if ( fn.length > 1 ) { - args = [ pseudo, pseudo, "", argument ]; - return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ? - markFunction(function( seed, matches ) { - var idx, - matched = fn( seed, argument ), - i = matched.length; - while ( i-- ) { - idx = indexOf( seed, matched[i] ); - seed[ idx ] = !( matches[ idx ] = matched[i] ); - } - }) : - function( elem ) { - return fn( elem, 0, args ); - }; - } - - return fn; - } - }, - - pseudos: { - // Potentially complex pseudos - "not": markFunction(function( selector ) { - // Trim the selector passed to compile - // to avoid treating leading and trailing - // spaces as combinators - var input = [], - results = [], - matcher = compile( selector.replace( rtrim, "$1" ) ); - - return matcher[ expando ] ? - markFunction(function( seed, matches, context, xml ) { - var elem, - unmatched = matcher( seed, null, xml, [] ), - i = seed.length; - - // Match elements unmatched by `matcher` - while ( i-- ) { - if ( (elem = unmatched[i]) ) { - seed[i] = !(matches[i] = elem); - } - } - }) : - function( elem, context, xml ) { - input[0] = elem; - matcher( input, null, xml, results ); - // Don't keep the element (issue #299) - input[0] = null; - return !results.pop(); - }; - }), - - "has": markFunction(function( selector ) { - return function( elem ) { - return Sizzle( selector, elem ).length > 0; - }; - }), - - "contains": markFunction(function( text ) { - text = text.replace( runescape, funescape ); - return function( elem ) { - return ( elem.textContent || elem.innerText || getText( elem ) ).indexOf( text ) > -1; - }; - }), - - // "Whether an element is represented by a :lang() selector - // is based solely on the element's language value - // being equal to the identifier C, - // or beginning with the identifier C immediately followed by "-". - // The matching of C against the element's language value is performed case-insensitively. - // The identifier C does not have to be a valid language name." - // http://www.w3.org/TR/selectors/#lang-pseudo - "lang": markFunction( function( lang ) { - // lang value must be a valid identifier - if ( !ridentifier.test(lang || "") ) { - Sizzle.error( "unsupported lang: " + lang ); - } - lang = lang.replace( runescape, funescape ).toLowerCase(); - return function( elem ) { - var elemLang; - do { - if ( (elemLang = documentIsHTML ? - elem.lang : - elem.getAttribute("xml:lang") || elem.getAttribute("lang")) ) { - - elemLang = elemLang.toLowerCase(); - return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0; - } - } while ( (elem = elem.parentNode) && elem.nodeType === 1 ); - return false; - }; - }), - - // Miscellaneous - "target": function( elem ) { - var hash = window.location && window.location.hash; - return hash && hash.slice( 1 ) === elem.id; - }, - - "root": function( elem ) { - return elem === docElem; - }, - - "focus": function( elem ) { - return elem === document.activeElement && (!document.hasFocus || document.hasFocus()) && !!(elem.type || elem.href || ~elem.tabIndex); - }, - - // Boolean properties - "enabled": createDisabledPseudo( false ), - "disabled": createDisabledPseudo( true ), - - "checked": function( elem ) { - // In CSS3, :checked should return both checked and selected elements - // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked - var nodeName = elem.nodeName.toLowerCase(); - return (nodeName === "input" && !!elem.checked) || (nodeName === "option" && !!elem.selected); - }, - - "selected": function( elem ) { - // Accessing this property makes selected-by-default - // options in Safari work properly - if ( elem.parentNode ) { - elem.parentNode.selectedIndex; - } - - return elem.selected === true; - }, - - // Contents - "empty": function( elem ) { - // http://www.w3.org/TR/selectors/#empty-pseudo - // :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5), - // but not by others (comment: 8; processing instruction: 7; etc.) - // nodeType < 6 works because attributes (2) do not appear as children - for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { - if ( elem.nodeType < 6 ) { - return false; - } - } - return true; - }, - - "parent": function( elem ) { - return !Expr.pseudos["empty"]( elem ); - }, - - // Element/input types - "header": function( elem ) { - return rheader.test( elem.nodeName ); - }, - - "input": function( elem ) { - return rinputs.test( elem.nodeName ); - }, - - "button": function( elem ) { - var name = elem.nodeName.toLowerCase(); - return name === "input" && elem.type === "button" || name === "button"; - }, - - "text": function( elem ) { - var attr; - return elem.nodeName.toLowerCase() === "input" && - elem.type === "text" && - - // Support: IE<8 - // New HTML5 attribute values (e.g., "search") appear with elem.type === "text" - ( (attr = elem.getAttribute("type")) == null || attr.toLowerCase() === "text" ); - }, - - // Position-in-collection - "first": createPositionalPseudo(function() { - return [ 0 ]; - }), - - "last": createPositionalPseudo(function( matchIndexes, length ) { - return [ length - 1 ]; - }), - - "eq": createPositionalPseudo(function( matchIndexes, length, argument ) { - return [ argument < 0 ? argument + length : argument ]; - }), - - "even": createPositionalPseudo(function( matchIndexes, length ) { - var i = 0; - for ( ; i < length; i += 2 ) { - matchIndexes.push( i ); - } - return matchIndexes; - }), - - "odd": createPositionalPseudo(function( matchIndexes, length ) { - var i = 1; - for ( ; i < length; i += 2 ) { - matchIndexes.push( i ); - } - return matchIndexes; - }), - - "lt": createPositionalPseudo(function( matchIndexes, length, argument ) { - var i = argument < 0 ? argument + length : argument; - for ( ; --i >= 0; ) { - matchIndexes.push( i ); - } - return matchIndexes; - }), - - "gt": createPositionalPseudo(function( matchIndexes, length, argument ) { - var i = argument < 0 ? argument + length : argument; - for ( ; ++i < length; ) { - matchIndexes.push( i ); - } - return matchIndexes; - }) - } -}; - -Expr.pseudos["nth"] = Expr.pseudos["eq"]; - -// Add button/input type pseudos -for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) { - Expr.pseudos[ i ] = createInputPseudo( i ); -} -for ( i in { submit: true, reset: true } ) { - Expr.pseudos[ i ] = createButtonPseudo( i ); -} - -// Easy API for creating new setFilters -function setFilters() {} -setFilters.prototype = Expr.filters = Expr.pseudos; -Expr.setFilters = new setFilters(); - -tokenize = Sizzle.tokenize = function( selector, parseOnly ) { - var matched, match, tokens, type, - soFar, groups, preFilters, - cached = tokenCache[ selector + " " ]; - - if ( cached ) { - return parseOnly ? 0 : cached.slice( 0 ); - } - - soFar = selector; - groups = []; - preFilters = Expr.preFilter; - - while ( soFar ) { - - // Comma and first run - if ( !matched || (match = rcomma.exec( soFar )) ) { - if ( match ) { - // Don't consume trailing commas as valid - soFar = soFar.slice( match[0].length ) || soFar; - } - groups.push( (tokens = []) ); - } - - matched = false; - - // Combinators - if ( (match = rcombinators.exec( soFar )) ) { - matched = match.shift(); - tokens.push({ - value: matched, - // Cast descendant combinators to space - type: match[0].replace( rtrim, " " ) - }); - soFar = soFar.slice( matched.length ); - } - - // Filters - for ( type in Expr.filter ) { - if ( (match = matchExpr[ type ].exec( soFar )) && (!preFilters[ type ] || - (match = preFilters[ type ]( match ))) ) { - matched = match.shift(); - tokens.push({ - value: matched, - type: type, - matches: match - }); - soFar = soFar.slice( matched.length ); - } - } - - if ( !matched ) { - break; - } - } - - // Return the length of the invalid excess - // if we're just parsing - // Otherwise, throw an error or return tokens - return parseOnly ? - soFar.length : - soFar ? - Sizzle.error( selector ) : - // Cache the tokens - tokenCache( selector, groups ).slice( 0 ); -}; - -function toSelector( tokens ) { - var i = 0, - len = tokens.length, - selector = ""; - for ( ; i < len; i++ ) { - selector += tokens[i].value; - } - return selector; -} - -function addCombinator( matcher, combinator, base ) { - var dir = combinator.dir, - skip = combinator.next, - key = skip || dir, - checkNonElements = base && key === "parentNode", - doneName = done++; - - return combinator.first ? - // Check against closest ancestor/preceding element - function( elem, context, xml ) { - while ( (elem = elem[ dir ]) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - return matcher( elem, context, xml ); - } - } - return false; - } : - - // Check against all ancestor/preceding elements - function( elem, context, xml ) { - var oldCache, uniqueCache, outerCache, - newCache = [ dirruns, doneName ]; - - // We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching - if ( xml ) { - while ( (elem = elem[ dir ]) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - if ( matcher( elem, context, xml ) ) { - return true; - } - } - } - } else { - while ( (elem = elem[ dir ]) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - outerCache = elem[ expando ] || (elem[ expando ] = {}); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ elem.uniqueID ] || (outerCache[ elem.uniqueID ] = {}); - - if ( skip && skip === elem.nodeName.toLowerCase() ) { - elem = elem[ dir ] || elem; - } else if ( (oldCache = uniqueCache[ key ]) && - oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) { - - // Assign to newCache so results back-propagate to previous elements - return (newCache[ 2 ] = oldCache[ 2 ]); - } else { - // Reuse newcache so results back-propagate to previous elements - uniqueCache[ key ] = newCache; - - // A match means we're done; a fail means we have to keep checking - if ( (newCache[ 2 ] = matcher( elem, context, xml )) ) { - return true; - } - } - } - } - } - return false; - }; -} - -function elementMatcher( matchers ) { - return matchers.length > 1 ? - function( elem, context, xml ) { - var i = matchers.length; - while ( i-- ) { - if ( !matchers[i]( elem, context, xml ) ) { - return false; - } - } - return true; - } : - matchers[0]; -} - -function multipleContexts( selector, contexts, results ) { - var i = 0, - len = contexts.length; - for ( ; i < len; i++ ) { - Sizzle( selector, contexts[i], results ); - } - return results; -} - -function condense( unmatched, map, filter, context, xml ) { - var elem, - newUnmatched = [], - i = 0, - len = unmatched.length, - mapped = map != null; - - for ( ; i < len; i++ ) { - if ( (elem = unmatched[i]) ) { - if ( !filter || filter( elem, context, xml ) ) { - newUnmatched.push( elem ); - if ( mapped ) { - map.push( i ); - } - } - } - } - - return newUnmatched; -} - -function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) { - if ( postFilter && !postFilter[ expando ] ) { - postFilter = setMatcher( postFilter ); - } - if ( postFinder && !postFinder[ expando ] ) { - postFinder = setMatcher( postFinder, postSelector ); - } - return markFunction(function( seed, results, context, xml ) { - var temp, i, elem, - preMap = [], - postMap = [], - preexisting = results.length, - - // Get initial elements from seed or context - elems = seed || multipleContexts( selector || "*", context.nodeType ? [ context ] : context, [] ), - - // Prefilter to get matcher input, preserving a map for seed-results synchronization - matcherIn = preFilter && ( seed || !selector ) ? - condense( elems, preMap, preFilter, context, xml ) : - elems, - - matcherOut = matcher ? - // If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results, - postFinder || ( seed ? preFilter : preexisting || postFilter ) ? - - // ...intermediate processing is necessary - [] : - - // ...otherwise use results directly - results : - matcherIn; - - // Find primary matches - if ( matcher ) { - matcher( matcherIn, matcherOut, context, xml ); - } - - // Apply postFilter - if ( postFilter ) { - temp = condense( matcherOut, postMap ); - postFilter( temp, [], context, xml ); - - // Un-match failing elements by moving them back to matcherIn - i = temp.length; - while ( i-- ) { - if ( (elem = temp[i]) ) { - matcherOut[ postMap[i] ] = !(matcherIn[ postMap[i] ] = elem); - } - } - } - - if ( seed ) { - if ( postFinder || preFilter ) { - if ( postFinder ) { - // Get the final matcherOut by condensing this intermediate into postFinder contexts - temp = []; - i = matcherOut.length; - while ( i-- ) { - if ( (elem = matcherOut[i]) ) { - // Restore matcherIn since elem is not yet a final match - temp.push( (matcherIn[i] = elem) ); - } - } - postFinder( null, (matcherOut = []), temp, xml ); - } - - // Move matched elements from seed to results to keep them synchronized - i = matcherOut.length; - while ( i-- ) { - if ( (elem = matcherOut[i]) && - (temp = postFinder ? indexOf( seed, elem ) : preMap[i]) > -1 ) { - - seed[temp] = !(results[temp] = elem); - } - } - } - - // Add elements to results, through postFinder if defined - } else { - matcherOut = condense( - matcherOut === results ? - matcherOut.splice( preexisting, matcherOut.length ) : - matcherOut - ); - if ( postFinder ) { - postFinder( null, results, matcherOut, xml ); - } else { - push.apply( results, matcherOut ); - } - } - }); -} - -function matcherFromTokens( tokens ) { - var checkContext, matcher, j, - len = tokens.length, - leadingRelative = Expr.relative[ tokens[0].type ], - implicitRelative = leadingRelative || Expr.relative[" "], - i = leadingRelative ? 1 : 0, - - // The foundational matcher ensures that elements are reachable from top-level context(s) - matchContext = addCombinator( function( elem ) { - return elem === checkContext; - }, implicitRelative, true ), - matchAnyContext = addCombinator( function( elem ) { - return indexOf( checkContext, elem ) > -1; - }, implicitRelative, true ), - matchers = [ function( elem, context, xml ) { - var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || ( - (checkContext = context).nodeType ? - matchContext( elem, context, xml ) : - matchAnyContext( elem, context, xml ) ); - // Avoid hanging onto element (issue #299) - checkContext = null; - return ret; - } ]; - - for ( ; i < len; i++ ) { - if ( (matcher = Expr.relative[ tokens[i].type ]) ) { - matchers = [ addCombinator(elementMatcher( matchers ), matcher) ]; - } else { - matcher = Expr.filter[ tokens[i].type ].apply( null, tokens[i].matches ); - - // Return special upon seeing a positional matcher - if ( matcher[ expando ] ) { - // Find the next relative operator (if any) for proper handling - j = ++i; - for ( ; j < len; j++ ) { - if ( Expr.relative[ tokens[j].type ] ) { - break; - } - } - return setMatcher( - i > 1 && elementMatcher( matchers ), - i > 1 && toSelector( - // If the preceding token was a descendant combinator, insert an implicit any-element `*` - tokens.slice( 0, i - 1 ).concat({ value: tokens[ i - 2 ].type === " " ? "*" : "" }) - ).replace( rtrim, "$1" ), - matcher, - i < j && matcherFromTokens( tokens.slice( i, j ) ), - j < len && matcherFromTokens( (tokens = tokens.slice( j )) ), - j < len && toSelector( tokens ) - ); - } - matchers.push( matcher ); - } - } - - return elementMatcher( matchers ); -} - -function matcherFromGroupMatchers( elementMatchers, setMatchers ) { - var bySet = setMatchers.length > 0, - byElement = elementMatchers.length > 0, - superMatcher = function( seed, context, xml, results, outermost ) { - var elem, j, matcher, - matchedCount = 0, - i = "0", - unmatched = seed && [], - setMatched = [], - contextBackup = outermostContext, - // We must always have either seed elements or outermost context - elems = seed || byElement && Expr.find["TAG"]( "*", outermost ), - // Use integer dirruns iff this is the outermost matcher - dirrunsUnique = (dirruns += contextBackup == null ? 1 : Math.random() || 0.1), - len = elems.length; - - if ( outermost ) { - outermostContext = context === document || context || outermost; - } - - // Add elements passing elementMatchers directly to results - // Support: IE<9, Safari - // Tolerate NodeList properties (IE: "length"; Safari: