import numpy as np
import matplotlib.pyplot as plt

start = np.array([0, 0])
bias = np.array([-3.37, 0.125])
weights = np.array([
    [-4,1.5],
    [-1.5,0]
])

def activate(input):
    # calculate activation as matrix
    # o1 = w11 * o1 + w12 * o2 + b1
    # o2 = w21 * o1 + w22 * o2 + b2
    return np.matmul(weights, input) + bias
        
def predict(n):
    current_output = start.copy()
    points = np.zeros((2, n))

    for i in range(n):
        # calculate output with tanh(x)
        current_output = np.tanh(activate(current_output))

        #save datapoint
        points[:, i] = current_output.copy()
    
    return points

timespan = 50
timespan_range = range(timespan)

predictions = predict(timespan)

# fetch o1, o2 from datapoint prediction
o1 = predictions[0, :]
o2 = predictions[1, :]

plt.title("Recurrent Neural Network")
plt.plot(timespan_range, o1)
plt.plot(timespan_range, o2)
plt.legend(["o1", "o2"], loc="upper left")
plt.show()