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#import os
#os.chdir('/home/aotsvetkov/programms/physlearn')


    

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#os.getcwd()


    

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# Необходмые команды импорта.
import sys
sys.path.append('../../physlearn/')
sys.path.append('../../source')

from physlearn.NeuralNet import NeuralNet
from physlearn.Optimizer import Optimizer
import numpy
import math
import matplotlib.pyplot as plt
from matplotlib import rc
import tensorflow as tf
import scipy
from IPython.display import clear_output

import pickle

%matplotlib inline


    

    




D:\Anaconda\lib\site-packages\h5py\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters

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#rc('text', usetex=True)


    

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def save_data(folder_name, data, file_name):
    cur_dir = os.getcwd()
    try:
        os.mkdir(folder_name)
    except OSError:
        pass
    os.chdir(folder_name)
    with open(file_name, 'wb') as f:
        pickle.dump(data, f)
    
    os.chdir(cur_dir)


    

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k = 1.0
m = 1.0
omega_sq = k / m
omega = math.sqrt(omega_sq)
x0 = 0.0
speed0 = 1.0


    

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if (speed0 != 0) and (x0 != 0):
    phi0 = math.atan(x0 * omega / speed0)
    A = x0 / (math.sin(phi0))
elif (x0 == 0.0) and (speed0 != 0):
    phi0 = 0.0
    A = speed0 / (omega * math.cos(phi0))
elif (speed0 == 0) and (x0 != 0):
    phi0 = math.pi / 2
    A = x0 / (math.sin(phi0))
else:
    phi0 = 0.0
    A = 0


    

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begin_hamiltonian = (k * (x0 ** 2) / 2) + (m * (speed0 ** 2) / 2)


    

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amount_of_points = 30
batch_size = 30
min_time = 0
max_time = 2 * math.pi / math.sqrt(omega_sq)


    

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t_list = numpy.linspace(min_time, max_time, amount_of_points).reshape((1, amount_of_points))
x_expected = A * numpy.sin(omega * t_list + phi0)


    

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t_cv = numpy.linspace(min_time, max_time, 1000).reshape((1, 1000))


    

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net = NeuralNet(-5, 5)


    

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net.add_input_layer(1)
net.add(10, net.sigmoid)
net.add_output_layer(1, net.linear)


    

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net.compile()


    

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dim = net.return_unroll_dim()


    

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def return_cost():
    y = net.return_graph()
    speed = tf.gradients(y, net.x)[0]
    acc = tf.gradients(speed, net.x)[0]
    #print(y)
    equation_part = tf.reduce_sum((acc + omega_sq * y) ** 2)
    begin_part = ((y[0][0] - x0) ** 2) + ((speed[0][0] - speed0) ** 2)
    current_hamiltonian = (k * (y ** 2) / 2) + (m * (speed ** 2) / 2)
    hamilton_part = tf.reduce_sum((current_hamiltonian - begin_hamiltonian) ** 2)
    cost_tensor = begin_part + (1 / amount_of_points) * ((equation_part) + hamilton_part)
    return cost_tensor


    

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def cost(params):
    net.roll_matrixes(params)
    return return_cost()


    

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opt = Optimizer()


    

    




---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-18-d0135b2612db> in <module>()
----> 1 opt = Optimizer()

TypeError: 'module' object is not callable

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opt.params_dict['number_of_individuals'] = 433
opt.params_dict['sess'] = net.sess
opt.params_dict['placeholder'] = net.x
opt.params_dict['x'] = t_list


    

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res = opt.optimize(cost, dim, optimizer=opt.diff_evolution_tf, max_time=200, min_func_value=1e-4)
net.roll_matrixes(res.x)


    

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res


    

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x_pred = net.run(t_cv)
plt.plot(t_cv[0], x_pred[0])
plt.plot(t_list[0], x_expected[0], 'x')


    

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plt.plot(res.cost_list)


    

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time_list = res.misc['time_list']


    

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speed = [i / time_list[i] for i in range(1, len(time_list))]
i_list = [i for i in range(1, len(time_list))]


    

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plt.plot(i_list, speed)


    

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numpy.arange(5, 448, 16)