import numpy as np
import math
import matplotlib.pyplot as plt
import random

dataset_size = 1000


data_0 = np.array([(max(0, np.random.normal(3, 0.5)), np.random.uniform(0, math.pi * 2))  for x in range(dataset_size)])
data_1 = np.array([(max(0, np.random.normal(6, 0.5)), np.random.uniform(0, math.pi * 2))  for x in range(dataset_size)])
train_y= np.concatenate([np.zeros(dataset_size), np.ones(dataset_size)])
train_x = np.concatenate([data_0, data_1], axis=0)
together = list(zip(train_x, train_y))
print('?', together[0])
#print(together)
random.shuffle(together)
together = [ (np.array([math.cos(x[0][1]) * x[0][0], math.sin(x[0][1]) * x[0][0]]), x[1]) for x in together]
tmp = np.array(together)
print('!', tmp.shape)
print('?', together[0])
train_x = np.array([x[0] for x in together])
train_y = np.array([x[1] for x in together])
print(train_y.shape)
print(train_x.shape)
train_y = train_y.astype(dtype=np.int32)
print(train_y)
print(max(train_y))
print(train_y.dtype)


color_name = 'rb'
color_letter = [color_name[x] for x in train_y]
#plt.scatter(train_x[:, 0], train_x[:, 1], color=color_letter)
#plt.show()


num_units = [2, 20, 20, 1]
num_layers = len(num_units) - 1

var_dict = {}
for i in range(num_layers):
    var_dict['w_' + str(i)] = np.random.normal(0, 0.05, (num_units[i], num_units[i + 1]))
    var_dict['b_' + str(i)] = np.zeros((num_units[i + 1],))


#for key, value in var_dict.items():
#print(key, value.shape)

def dense_forward(x, layer_id):
    x = np.matmul(x, w) + b
    return x

def relu_forward(x, layer_id):
    x = np.max(x, 0)
    return x

def sigmoid_forward(x):
    x = np.exp(-x)
    x = 1.0 / (1 + x)
    return x


batch_size = 32


for i in range(1000):
    idx = np.random.randint(0, train_x.shape[0], batch_size)
    batch_x = train_x[idx]
    batch_y = train_y[idx]
    backprop_dense = []
    backprop_relu = []
    backprop_sigmoid
    for i in range(num_layers):
        batch_x, mem = dense_forward(batch_x, i)
        backprop_dense.append(mem)
        if i + 1 != num_layers:
            batch_x, mem = relu_forward(batch_x, i)
            backprop_relu.append(mem)

        batch_x, mem = sigmoid_forward(batch_x)
        backpro_sigmoid = mem




print(data_0)