commit chapter9

chapter9/code.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Nov  3 13:10:35 2017
+
+@author: lu
+"""
+
+import pandas as pd
+from numpy.random import shuffle
+from sklearn import svm
+import pickle
+from sklearn import metrics
+
+"""
+programmer_1-->svm支持向量机
+
+"""
+
+
+def programmer_1():
+
+    inputfile = "data/moment.csv"
+    outputfile1 = "tmp/cm_train.xls"
+    outputfile2 = "tmp/cm_test.xls"
+    data = pd.read_csv(inputfile, encoding="gbk")
+    data = data.as_matrix()
+
+    # 随即抽取训练集和验证集--8:2
+    shuffle(data)
+    data_train = data[:int(0.8 * len(data)), :]
+    data_test = data[int(0.8 * len(data)):, :]
+    # 训练集/验证集的训练数据和结果数据的抽取
+    x_train = data_train[:, 2:] * 30
+    y_train = data_train[:, 0].astype(int)
+    x_test = data_test[:, 2:] * 30
+    y_test = data_test[:, 0].astype(int)
+    # 训练支持向量机的SVC
+    model = svm.SVC()
+    model.fit(x_train, y_train)
+
+    # 保存模型
+    pickle.dump(model, open("tmp/svm.model", "wb"))
+    # 混淆矩阵，评估模型的准确性
+    cm_train = metrics.confusion_matrix(y_train,  model.predict(x_train))
+    cm_test = metrics.confusion_matrix(y_test, model.predict(x_test))
+
+    pd.DataFrame(cm_train, index = list(range(1, 6)), columns = list(range(1, 6))).to_excel(outputfile1)
+    pd.DataFrame(cm_test, index = list(range(1, 6)), columns = list(range(1, 6))).to_exceli(outputfile2)
+
+
+
+if __name__ == "__main__":
+    programmer_1()
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