Why my perceptron doesn't train well and produces bad results on test data?

Question

I am a newbie in Machine learning and I am writing a small code for Perceptron. This is the first time I am writing code in Python. I have four training data points (X). As they are used for supervised learning so, each data point has its corresponding correct output pair (D). I have implemented SGD and used generalized Delta rule (wij ← wij + α δixj). I have trained my perceptron 10,000 times (epochs= 10,000).
Although everything looks fine to me, I don't get the right results when I test it with test values. I need some suggestions so that I can improve my results on test data. P.S. How can I improve this code?

Code

import numpy as np 

def sigmoid(x):
return 1 / (1 + np.exp(-x))    

def Delta_SGD(W, X, D):
 N = 4
 for x in range(N):

    v1 = np.dot(X[x][0], W[0])
    v2 = np.dot(X[x][1], W[1])
    v3 = np.dot(X[x][2], W[2])
    #weighted sum
    V = v1+v2+v3

    #output of neuron
    y = sigmoid(V)

    #error 
    e = D[x] - y

    #derivative of sigmoid(y)
    delta = (y)*(1-y)*e

    #Delta rule
    DW = alpha*delta*X[x]

    #updated weights
    W[0] = W[0] + DW[0]
    W[1] = W[1] + DW[1]
    W[2] = W[2] + DW[2]

return W

#input data points
X = np.array([ [0,0,1],[0,1,1],[1,0,1],[1,1,1] ])

#Correct output pairs
D = np.array([[0,0,1,1]]).T

#learning rate
alpha = 0.9

#random weights
W =  2*np.random.random((3,1)) - 1

#10000 epochs
for epoch in range(10000):
 W = Delta_SGD(W, X, D)
 print(epoch)

#Final weights after all epochs
print("Final weights are \n", W)

#testing network
N = 4
for x in range(N):

 v1 = np.dot(X[x][0], W[0])
 v2 = np.dot(X[x][1], W[1])
 v3 = np.dot(X[x][2], W[2])

 V = v1+v2+v3
 y = sigmoid(V)
 print("output of neuron is \n ", y)

Answer 1

Two points about the whole thing

1. You did not test yet. The point behind the training process is to make machine able to learn from the data conditioned on the ability of generalizing this to predicting samples which it has not seen before. Otherwise, the good training is actually overfitting. So here you trained on X and you need to create new samples and check the result to really call it testing (this is an introductory explanation).
2. Machine Learning is about features a lot! Playing with features and cleaning, modifying and filtering them is a key point. In your example, the last dimension of your 3d data is always 1. Does it distinguish anything? (in my course you get a complete explanation of this in the Lecture 2). So that feature (dimenstion, element of the vector) can/should be removed. To better understanding, imagine your 3d spread of the data. The z axis is always 1 which means the topology of points is what you see in x-y plain. So use only that one.