梯度下降法

linear/gradient_linear.py

@@ -0,0 +1,45 @@
+#!/usr/bin/python
+# -*- coding: UTF-8 -*-
+'''
+最简单的mse
+'''
+import sys
+reload(sys)
+sys.setdefaultencoding('utf-8')
+
+import random
+import numpy as np
+from sklearn.linear_model import LinearRegression
+from sklearn import metrics
+from draw import draw_util
+
+
+def read_data(path):
+    with open(path) as f :
+        lines=f.readlines()
+    lines=[eval(line.strip()) for line in lines]
+    return lines
+
+
+def cal_step_pow(data, w, b=3):
+    p = [(w*item[0][0] + b - item[1][0])*item[0][0]*2  for item in data]
+    sum_p = sum(p)
+    return sum_p/len(data)
+
+def cal_mse(data, w, b=3):
+    sum_p = sum([(w * item[0][0] + b - item[1][0]) * (w * item[0][0] + b - item[1][0]) for item in data])
+    return sum_p / len(data)
+
+def train():
+    train_data = read_data('train_data')
+    w = random.uniform(-50, 50)
+    for i in range(10):
+        step = cal_step_pow(train_data, w)*0.01
+        mse = cal_mse(train_data, w)
+        print w, step, mse
+
+        w = w - step
+
+
+if __name__ == '__main__':
+    train()