class MySupportVectorMachine:

    def __init__(self):
        self.w_vector = []
        self.bias = 0
        self.support_ = []

    #---------------------------------------------------
    def get_support_vector_indices(self,Xdata, Ydata):


         #pass
    #---------------------------------------------------
    def fit(self,Xdata,Ydata):
        #Initialize our SVMs weight vector with zeros
        w = np.zeros(len(Xdata[0]))
        #The learning rate
        eta = 0.0001
        #how many iterations to train for
        epochs = 1000
        #store misclassifications so we can plot how they change over time
        bias = 0
        C = 0.001
        print("nnTraining... ",end ="" )
        #training part, gradient descent part
        for epoch in range(1,epochs):
            if epoch %1000 == 0:
                print("." , end = "")
            for i, x in enumerate(Xdata):
                #misclassification
                if Ydata[i] == 0:
                    Ydata[i]= -1

                if (Ydata[i]*np.dot(Xdata[i], w) + bias) < 1:
                    #misclassified update for ours weights
                    w = w + eta * ( C *(Xdata[i] * Ydata[i]) + (-2  *(1/epoch)* w) )
                    bias = bias + eta * (Ydata[i] * C)
                else:
                    #correct classification, update our weights
                    w = w + eta * (-2  *(1/epoch)* w)
                if Ydata[i] == -1:
                    Ydata[i]= 0
        self.w_vector = w
        self.bias = bias
        print("ntttSVM is Successfully Trained...n")



        self.get_support_vector_indices(Xdata, Ydata)

    #---------------------------------------------------
    def predict(self, XtestData):
        #print("nn w_vector: n",self.w_vector)
        #print("nn bias: n",self.bias)
        predictedDataList = []
        for xi in XtestData:
            predictedData = np.sign(np.dot(self.w_vector,xi) + self.bias).astype(int)
            if predictedData == -1:
                predictedData = 0
            predictedDataList.append(predictedData)
        return(np.array(predictedDataList))

    #---------------------------------------------------

# End of the Class