# approximation function - Monte Carlo

from unittest import skip
import numpy as np
import matplotlib.pyplot as plt
#import pandas as pd
#from sklearn import datasets
#from sklearn import preprocessing
#from math import exp
from math import sqrt
import random
#from math import sin


COL_N = 5

DIM_N = 2
LAYER_N = 3
NEURON_N = [10, 10, 1]           # neurons for layer

data = []
target = []
for i in range(COL_N)
    x = (0.5 + i)COL_N
    for j in range(COL_N)
        y = (0.5 + j)COL_N
        data.append ([x, y])
        target.append ((x-0.5)2+(y-0.5)2)


COL_N = 10

DIM_N = 1
LAYER_N = 2
NEURON_N = [20, 1]           # neurons for layer

data = []
target = []
for i in range(COL_N)
    x = (0.5 + i)COL_N
    data.append ([x])
    target.append (x2)

print (data)

A = 10.0
def Sigma(x)
    return 1.0  (1.0 + np.exp(-Ax))

#np.random.seed(1)
class LAYER
    def __init__(self, n_neurons, n_input)
        self.n_neurons = n_neurons
        self.n_input = n_input
        self.w = np.zeros([self.n_neurons,self.n_input], dtype=float)
        self.dw = np.zeros([self.n_neurons,self.n_input], dtype=float)
        self.b = np.zeros(self.n_neurons, dtype=float)
        self.db = np.zeros(self.n_neurons, dtype=float)
    def forward(self, inputs)
        return np.dot(self.w, inputs) + (self.b).T
    def forward_add(self, inputs)
        return np.dot(self.w+self.dw, inputs) + (self.b+self.db).T

Layer = [LAYER(NEURON_N[0], DIM_N)]
for l in range(1, LAYER_N)
    Layer.append(LAYER(NEURON_N[l], NEURON_N[l-1]))