def ContactLensesClassifier():
fr = open('lenses.txt')
lines = fr.readlines()
dataSet = [l.strip().split('\t') for l in lines]
labels = ['age', 'prescript', 'astigmatic', 'tearRate']
decisionTree = createTree(dataSet, labels)
print(labels)
plotDecisionTree.createPlot(decisionTree)
storeTree(decisionTree, 'lensesDecisionTree.txt')
tree = getTree('lensesDecisionTree.txt')
plotDecisionTree.createPlot(tree)
labels = data_set.labels
for i in range(len(data_set.feature_set[0])):
new_feature = []
for feature in data_set.feature_set:
if feature[i] != '-':
new_feature.append(feature[i])
features_list[labels[i]] = set(new_feature)
return features_list
df = pd.read_csv('watermelon_4_2a.csv')
data = df.values[:, :].tolist()
# data_test = df.values[11:, :].tolist()
labels = df.columns.values[0:-1].tolist()
data_set = DataSet(data, labels,[1]*len(data))
feature_dict = generate_full_features(data_set)
tree = DecisionTree(data_set,feature_dict)
plotDecisionTree.createPlot(tree)
# print tree.node.children[0].children[0].children[1].children
# coding:utf-8
# 对隐形眼镜的分类
import decisionTree
import plotDecisionTree
import trees
fr = open('lenses.txt')
lenses = [inst.strip().split('\t') for inst in fr.readlines()]
fr.close()
print type(lenses)
for i in range(len(lenses)):
print lenses[i]
print '... end lenses'
lensesFeatName = ['age', 'prescript', 'astigmatic', 'tearRate', 'lenses type']
lensesTree = decisionTree.createTree(lenses, lensesFeatName)
print lensesTree
plotDecisionTree.createPlot(lensesTree)
# coding:utf-8
# 对隐形眼镜的分类
import decisionTree
import plotDecisionTree
import trees
fr = open('lenses.txt')
lenses = [inst.strip().split('\t') for inst in fr.readlines()]
fr.close()
print type(lenses)
for i in range(len(lenses)):
print lenses[i]
print '... end lenses'
lensesFeatName = ['age', 'prescript', 'astigmatic', 'tearRate', 'lenses type']
lensesTree = decisionTree.createTree(lenses, lensesFeatName)
print lensesTree
plotDecisionTree.createPlot(lensesTree)