ANN

import numpy as np 
feature_set = np.array([[0,1,0],[0,0,1],[1,0,0],[1,1,0],[1,1,1]]) 
labels = np.array([[1,0,0,1,1]]) 
labels = labels.reshape(5,1)

#define hyper parameters for our neural network

np.random.seed(42)  #random.seed function so that we can get the same random values whenever the script is executed.
weights = np.random.rand(3,1) 
bias = np.random.rand(1) 
lr = 0.05

def sigmoid(x):    #activation function is the sigmoid function
    return 1/(1+np.exp(-x))

def sigmoid_der(x):    #calculates the derivative of the sigmoid function
    return sigmoid(x)*(1-sigmoid(x))

#train our neural network that will be able to predict whether a person is
#obese or not.

# An epoch is basically the number of times we want to train the algorithm on our data.
#We will train the algorithm on our data 20,000 times. The ultimate goal i
#s to minimize the error.

for epoch in range(20000): 
    inputs = feature_set

#Here we find the dot product of the input and the weight vector and add bias to it.

    # feedforward step1
    XW = np.dot(feature_set, weights) + bias
   
#We pass the dot product through the sigmoid activation function

    #feedforward step2
    z = sigmoid(XW)
   
#The variable z contains the predicted outputs. The first step of the backpropagation is to find the error.

    # backpropagation step 1
    error = z - labels

    print(error.sum())

    # backpropagation step 2
    dcost_dpred = error
    dpred_dz = sigmoid_der(z)
   
#Here we have the z_delta variable, which contains the product of dcost_dpred and dpred_dz.
#Instead of looping through each record and multiplying the input with corresponding z_delta,
#we take the transpose of the input feature matrix and multiply it with the z_delta.
#Finally, we multiply the learning rate variable lr with the derivative to increase the speed of convergence.

    z_delta = dcost_dpred * dpred_dz

    inputs = feature_set.T
    weights -= lr * np.dot(inputs, z_delta)

    for num in z_delta:
        bias -= lr * num


#You can see that error is extremely small at the end of the
#training of our neural network.
#At this point of time our weights and bias will have values that can be used to detect whether a person is diabetic or not,
#based on his smoking habits, obesity, and exercise habits.

#TEST : suppose we have a record of a patient that comes
#in who smokes, is not obese, and doesn't exercise.
#Let's find if he is likely to be diabetic or not. The input feature will look like this: [1,0,0].

single_point = np.array([1,0,0]) 
result = sigmoid(np.dot(single_point, weights) + bias) 
print(result)