import numpy as np
from sklearn.datasets import load_digits
from sklearn.preprocessing import LabelBinarizer
from sklearn.cross_validation import train_test_split

def sigmoid(x):
    return 1/(1+np.exp(-x))
def dsigmoid(x):
    return x*(1-x)
class NeuralNetwork:
    def __init__(self,layers): #(64,100,10)
        #权值的初始化,范围-1到1
        self.V=np.random.random((layers[0]+1,layers[1]+1))*2-1
        self.W = np.random.random((layers[1] + 1, layers[2])) * 2 - 1

    def train(self,X,y,lr=0.11,epochs=10000):
        #添加偏置
        temp=np.ones([X.shape[0],X.shape[1]+1])
        temp[:,0:-1]=X
        X=temp
        for n in range(epochs+1):
            i=np.random.randint(X.shape[0])#随机选取一个数据
            x=[X[i]]
            x=np.atleast_2d(x) #转为2维数据
            L1=sigmoid(np.dot(x,self.V)) #隐藏层输出
            L2=sigmoid(np.dot(L1,self.W)) #输出层输出
            L2_delta=(y[i]-L2)*dsigmoid(L2)
            L1_delta=L2_delta.dot(self.W.T)*dsigmoid(L1)

            self.W+=lr*L1.T.dot(L2_delta)
            self.V+=lr*x.T.dot(L1_delta)

            #每训练1000次预测一次准确率
            if n%1000==0:
                predictions=[]
                for j in range(X_test.shape[0]):
                    o =self.predict(X_test[j])
                    predictions.append(np.argmax(o)) #获取预测结果
                accuracy =np.mean(np.equal(predictions,y_test))
                print('epoch:',n,'accuracy:',accuracy)
    def predict(self,x):
        #添加偏置
        temp=np.ones(x.shape[0]+1)
        temp[0:-1]=x
        x=temp
        x=np.atleast_2d(x)  #转为2维数据
        L1=sigmoid(np.dot(x,self.V)) #隐藏层输出
        L2=sigmoid(np.dot(L1,self.W)) #输出层输出
        return L2
digits =load_digits() #载入数据
X=digits.data #数据
y=digits.target #标签
#输入归一化
X-=X.min()
X/X.max()
nm=NeuralNetwork([64,100,10]) #创建网络
X_tain,X_test,y_train,y_test=train_test_split(X,y) #分割数据1/4测试，3/4训练
labels_train=LabelBinarizer().fit_transform(y_train) #标签二值化
labels_test =LabelBinarizer().fit_transform(y_test)
print('start')
nm.train(X_tain,labels_train,epochs=20000)
print('end')