multilayerNNsimple.py

#importing libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import preprocessing
from sklearn.preprocessing import normalize
import sklearn
import sklearn.datasets
import sklearn.linear_model

%matplotlib inline

np.random.seed(1)
#load dataset
dataset = pd.read_csv("/Users/samar/Documents/pythonwd/special-potato/AXISBANK.csv")


#Clean dataset
#adding t1 value or the next day value
dataset["t1"] = dataset["4. close"].shift(-1)


#removing last NaN - because nobody likes naan! 
dataset = dataset[0:(dataset.shape[0]-1)]


#adding p1 as change between t1 and t0
dataset["p1"] = (dataset["t1"] - dataset["4. close"]) 


#changing p1 to 1 if change was +ve and 0 if - ve 
dataset["p1"] = np.where(dataset["p1"] > 0, 1,0)


#cleaning out dates and dataframe
X = dataset[["1. open","2. high", "3. low" ,"4. close", "5. volume"]]

def normalize(df):
    G = preprocessing.StandardScaler().fit(df)
    ndf_mean = df- G.mean_
    ndf = ndf_mean/G.var_
    return ndf
    
X = normalize(X)

split = (int(X.shape[0]*(2/3)))
train_set_x = X[:split]
train_set_x = train_set_x.transpose()
train_set_x = train_set_x.values


test_set_x = X[split:]
test_set_x = test_set_x.transpose()
test_set_x = test_set_x.values

# similarly making y matrix with (1, m) size
Y = dataset[["p1"]]

train_set_y = Y[:split]
train_set_y = train_set_y.transpose()
train_set_y = train_set_y.values


test_set_y = Y[split:]
test_set_y = test_set_y.transpose()
test_set_y = test_set_y.values



np.shape(test_set_x)

X = train_set_x
Y = train_set_y

m = Y.shape[1]

def sigmoid(z):
    s = 1 / (1 + np.exp(-z))
    return s

n_x = X.shape[0]
#siz of output layer
n_y = Y.shape[0]
#size of hidden layer
n_h1= 4
n_h2 = 3

learning_rate = 0.1

W1 = np.random.randn(n_h1, n_x) * 0.01
b1 = np.zeros((n_h1,1))
W2 = np.random.randn(n_h2,n_h1) * 0.01
b2 = np.zeros((n_h2,1))
W3 = np.random.randn(n_y,n_h2) * 0.01
b3 = np.zeros((n_y,1))

for i in range(0, 10000):
    Z1 = np.dot(W1,X) + b1
    A1 = np.tanh(Z1)
    Z2 = np.dot(W2,A1) + b2
    A2 = np.tanh(Z2)
    Z3 = np.dot(W3,A2) + b3
    A3 = sigmoid(Z3)
    
    logprobs = np.multiply(np.log(A3), Y) + np.multiply((1 - Y), np.log(1 - A3))
    cost = - np.sum(logprobs) / m
    
    dZ3 = A3 - Y
    dW3 = (1 / m) * np.dot(dZ3, A2.T)
    db3 = (1 / m) * np.sum(dZ3, axis=1, keepdims=True)
    dZ2 = np.multiply(np.dot(W3.T, dZ3), 1 - np.power(A2, 2))
    dW2 = (1 / m) * np.dot(dZ2, A1.T)
    db2 = (1 / m) * np.sum(dZ2, axis=1, keepdims=True)
    dZ1 = np.multiply(np.dot(W2.T, dZ2), 1 - np.power(A1, 2))
    dW1 = (1 / m) * np.dot(dZ1, X.T)
    db1 = (1 / m) * np.sum(dZ1, axis=1, keepdims=True)
    
    
    W1 = W1 - learning_rate * dW1
    b1 = b1 - learning_rate * db1
    W2 = W2 - learning_rate * dW2
    b2 = b2 - learning_rate * db2
    
    print(cost)