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# Необходмые команды импорта.
import sys
sys.path.append('../physlearn/')
sys.path.append('../source')
import numpy as np
from numpy import linalg as LA
import tensorflow as tf
from matplotlib import pylab as plt
import numpy.random as rand
from physlearn.NeuralNet.NeuralNet import NeuralNet
from physlearn.Optimizer.NelderMead.NelderMead import NelderMead
import math_util
import math

from mpl_toolkits.mplot3d.axes3d import Axes3D
%matplotlib notebook

def make_all_variants(A):
    dim = A.shape[0]
    m = A.shape[1]
    res  = np.zeros((dim, m ** dim))
    for i in range(dim):
        for index, item in enumerate(A[i]):
            for j in range(i + 2):
                left_break = (m ** (dim - i - 1)) * index + j * m ** (dim - i)
                right_break = (m ** (dim - i - 1)) * (index + 1) + j * m ** (dim - i) 
                res[i, left_break:right_break] = item
    return res

def make_data(a,b, m_l, dim):
    x = np.empty((dim, m_l))
    for i in range(dim):
        x[i] = np.linspace(a, b, m_l)
    x_train = make_all_variants(x)
    return x_train

a = 0
b = 2*math.pi
k = 1
sigmoid_ammount = 25
m_1 = 10
space_dim = 3
iterations = int(5e5)
max_eps = 1e-12

x = make_data(a,b,m_1,space_dim)
m =x[0].size


    

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net = NeuralNet(-2,2)
net.add_input_layer(space_dim)
net.add(sigmoid_ammount, tf.sigmoid)
net.add_output_layer(1, net.linear)
net.compile()
net.set_random_matrixes()
net_out = net.return_graph()
sess = net.return_session()
dim = net.return_unroll_dim()

f = tf.cos(k*tf.sqrt(tf.reduce_sum(tf.square(net.x), axis = 0)))
J = (tf.reduce_sum(tf.square(f - tf.reduce_sum(net_out, axis = 0))))*(1/m) 

#f = tf.cos(k*x_tf)

def COST(params):
    net.roll_matrixes(params)
    res = net.calc(J, {net.x:x})
    return res



opt_nm = NelderMead(-2.5,2.5, progress_bar='tqdm')
opt_nm.set_epsilon_and_sd(0.3, 100)

def opt(J, dim, n_it, eps):
    optimisation_result = opt_nm.optimize(J, dim, n_it, eps)
    return optimisation_result


    

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optimisation_result = opt(COST, dim, iterations, max_eps)
print("J after optimisation: ", COST(optimisation_result.x))
print("Информация: ", optimisation_result)


    

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NN_grid = net.run(x).reshape(m)
f_true = net.calc(f, {net.x:x}).reshape(m)
error = NN_grid - f_true


    

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J_fin = optimisation_result.cost_function
mse = math_util.MSE(error)
std_err = math_util.std_err(error)
print(J_fin, ' ', mse, ' ', std_err)


    

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COST(optimisation_result.x)


    

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NN_grid


    

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f_true


    

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