Create ridge_regression_test.py

Author: zhaozhiyong19890102

Chapter_8 RidgeRegression/ridge_regression_test.py

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+#coding:UTF-8
+
+import numpy as np
+
+def load_data(file_path):
+    '''导入测试数据
+    input:  file_path(string):训练数据
+    output: feature(mat):特征
+    '''
+    f = open(file_path)
+    feature = []
+    for line in f.readlines():
+        feature_tmp = []
+        lines = line.strip().split("\t")
+        feature_tmp.append(1)  # x0
+        for i in xrange(len(lines)):
+            feature_tmp.append(float(lines[i]))
+        feature.append(feature_tmp)
+    f.close()
+    return np.mat(feature)
+
+def load_model(model_file):
+    '''导入模型
+    input:  model_file(string):线性回归模型
+    output: w(mat):权重值
+    '''
+    w = []
+    f = open(model_file)
+    for line in f.readlines():
+        w.append(float(line.strip()))
+    f.close()
+    return np.mat(w).T
+    
+def get_prediction(data, w):
+    '''对新数据进行预测
+    input:  data(mat):测试数据
+            w(mat):权重值
+    output: 最终的预测
+    '''
+    return data * w
+
+def save_result(file_name, predict):
+    '''保存最终的结果
+    input:  file_name(string):需要保存的文件
+            predict(mat):预测结果
+    '''
+    m = np.shape(predict)[0]
+    result = []
+    for i in xrange(m):
+        result.append(str(predict[i,0]))
+    f = open(file_name, "w")
+    f.write("\n".join(result))
+    f.close()
+
+if __name__ == "__main__":
+    # 1、导入测试数据
+    print "----------1.load data ------------"
+    testData = load_data("data.txt")
+    # 2、导入线性回归模型
+    print "----------2.load model ------------"
+    w = load_model("weights")
+    # 3、得到预测结果
+    print "----------3.get prediction ------------"
+    predict = get_prediction(testData, w)
+    # 4、保存最终的结果
+    print "----------4.save prediction ------------"
+    save_result("predict_result", predict)