import numpy as np
from sklearn.preprocessing import OneHotEncoder,LabelEncoder
import numpy as np
from sklearn.datasets import load_iris  #資料集
from sklearn.model_selection import train_test_split  #資料集的分割函式
from sklearn.preprocessing import StandardScaler      #資料預處理
from sklearn import metrics
from sklearn.model_selection import cross_validate
#引入包含資料驗證方法的包
from sklearn import metrics

class HiddenLayer:
    def __init__(self,x,num):
        row = x.shape[0]
        columns = x.shape[1]
        rnd = np.random.RandomState(4444)
        self.w = rnd.uniform(-1,1,(columns,num))
        self.b = np.zeros([row,num],dtype=float)
        for i in range(num):
            rand_b = rnd.uniform(-0.4,0.4)
            for j in range(row):
                self.b[j,i] = rand_b
        self.h = self.sigmoid(np.dot(x,self.w)+self.b)
        self.H_ = np.linalg.pinv(self.h)
        # print(self.H_.shape)
    def sigmoid(self,x):
        return 1.0 / (1 + np.exp(-x))

    def regressor_train(self,T):
        C = 2
        I = len(T)
        sub_former = np.dot(np.transpose(self.h), self.h) + I / C
        all_m = np.dot(np.linalg.pinv(sub_former),np.transpose(self.h))
        T = T.reshape(-1,1)
        self.beta = np.dot(all_m,T)
        return self.beta
    def classifisor_train(self,T):
        en_one = OneHotEncoder()
        T = en_one.fit_transform(T.reshape(-1,1)).toarray() #獨熱編碼之後一定要用toarray()轉換成正常的陣列
        C = 3
        I = len(T)
        sub_former = np.dot(np.transpose(self.h), self.h) + I / C
        all_m = np.dot(np.linalg.pinv(sub_former), np.transpose(self.h))
        self.beta = np.dot(all_m, T)
        return self.beta
    def regressor_test(self,test_x):
        b_row = test_x.shape[0]
        h = self.sigmoid(np.dot(test_x,self.w)+self.b[:b_row,:])
        result = np.dot(h,self.beta)
        return result
    def classifisor_test(self,test_x):
        b_row = test_x.shape[0]
        h = self.sigmoid(np.dot(test_x,self.w)+self.b[:b_row,:])
        result = np.dot(h,self.beta)
        result = [item.tolist().index(max(item.tolist())) for item in result]
        return result



stdsc = StandardScaler()
iris = load_iris()
x,y = stdsc.fit_transform(iris.data),iris.target
x_train, x_test, y_train, y_test = train_test_split(x , y, test_size=0.2, random_state=0)

a = HiddenLayer(x_train,20)
a.classifisor_train(y_train)

result = a.classifisor_test(x_test)

print(result)
print(metrics.accuracy_score(y_test,result))