improve

Neural_Network/neuralnetwork_bp3.py

@@ -9,7 +9,7 @@
 import numpy as np
 import matplotlib.pyplot as plt
 
-class Bpnw():
+class Bpnn():
 
     def __init__(self,n_layer1,n_layer2,n_layer3,rate_w=0.3,rate_t=0.3):
         '''
@@ -38,7 +38,7 @@ def sig_plain(self,x):
     def do_round(self,x):
         return round(x, 3)
 
-    def trian(self,patterns,data_train, data_teach, n_repeat, error_accuracy,draw_e = bool):
+    def trian(self,patterns,data_train, data_teach, n_repeat, error_accuracy, draw_e=False):
         '''
         :param patterns: the number of patterns
         :param data_train: training data x; numpy.ndarray
@@ -127,8 +127,26 @@ def predict(self,data_test):
 
 
 def main():
-    #I will fish the mian function later
-    pass
+    #example data
+    data_x = [[1,2,3,4],
+              [5,6,7,8],
+              [2,2,3,4],
+              [7,7,8,8]]
+    data_y = [[1,0,0,0],
+              [0,1,0,0],
+              [0,0,1,0],
+              [0,0,0,1]]
+
+    test_x = [[1,2,3,4],
+              [3,2,3,4]]
+
+    #building network model
+    model = Bpnn(4,10,4)
+    #training the model
+    model.trian(patterns=4,data_train=data_x,data_teach=data_y,
+                n_repeat=100,error_accuracy=0.1,draw_e=True)
+    #predicting data
+    model.predict(test_x)
 
 if __name__ == '__main__':
     main()