把测试数据和预测线画在一块

draw/draw_util.py

@@ -1,6 +1,7 @@
 #!/usr/bin/python
 # -*- coding: UTF-8 -*-
 import matplotlib.pyplot as plt
+import numpy as np
 
 #绘制散点图
 def drawScatter(heights,weights):
@@ -14,7 +15,31 @@ def drawScatter(heights,weights):
     plt.show()
 
 
+def drawLine(w, b):
+    plt.xlabel("x")
+    plt.ylabel("y")
+    x = np.arange(0, 10)
+    y = w*x + b
+
+    plt.plot(x, y)
+    plt.show()
+
+
+def drawScatterAndLine(p, q, w, b):
+    plt.scatter(p, q)
+    plt.xlabel('p')
+    plt.ylabel('q')
+    plt.title('line regesion')
+
+    x = np.arange(0, 11)
+    y = w * x + b
+
+    plt.plot(x, y, color='red')
+    plt.show()
+
+
 if __name__ == '__main__':
     heights = [1.5, 1.7]
     weights = [43, 61]
-    drawScatter(heights,weights)
+    drawScatter(heights,weights)
+    drawLine(1,2)

linear/train.py

@@ -68,6 +68,7 @@ def test():
 	with open("test_curve.csv","w") as f :
 		f.writelines("\n".join(test_curve))
 	'''
+	return X_train,y_train, model.coef_, model.intercept_
 
 
 def draw_line():
@@ -78,7 +79,12 @@ def draw_line():
 
 
 if __name__ == '__main__':
-	draw_line()
-	test()
+	# draw_line()
+	p, q, w,b = test()
+	p = [i[0] for i in p.tolist()]
+	q = [i[0] for i in q.tolist()]
+	w = w[0]
+	b = b[0]
+	draw_util.drawScatterAndLine(p, q, w, b)