# -*- coding: utf-8 -*-
"""
Created on Tue Dec 10 11:08:24 2019

@author: ShardeR
"""




import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
#from keras.models import Sequential
from sklearn.neural_network import MLPRegressor
from sklearn.model_selection import train_test_split
from pyswarms.utils.plotters import plot_cost_history, plot_contour, plot_surface
#from keras.layers import Dense, Dropout, LSTM
import matplotlib.pyplot as plt
import pyswarms as ps
import SwarmPackagePy as sp
from SwarmPackagePy import testFunctions as tf



df = pd.read_csv('aadr.us.txt', names=["Data","Open","High","Low","Close"], usecols=[0,1,2,3,4] , parse_dates=True, index_col=0)



t_train, val_train, t_target, val_target = train_test_split(df, df['Open'] , test_size=0.2 , shuffle=False)


t_target = np.array(t_target[1:])
val_train =  np.array(val_train[0:(len(val_train)-1)])
t_train = np.array(t_train[0:(len(t_train)-1)])
val_target = np.array(val_target[1:])

t_target = np.reshape(t_target,(len(t_target),1))

val_target = np.reshape(val_target,(len(val_target),1))


scaler = MinMaxScaler(feature_range=(0, 1))
t_target = scaler.fit_transform(t_target)
val_train = scaler.fit_transform(val_train)
t_train = scaler.fit_transform(t_train)
val_target = scaler.fit_transform(val_target)




#activation func  --- tanh , identity , logistic , relu
#solver ---- sgd , lbfgs , adam

def mlp_regress(hyperparamets):
    regress = MLPRegressor(hidden_layer_sizes=(100,) , activation='logistic', solver='adam', batch_size='auto', learning_rate_init=(hyperparamets))
    regress.fit(t_train,t_target.ravel())
    regress.predict(val_train)
    score = regress.score(val_train,val_target)
    loss = (1-score)
    print(regress)
    return loss

def f(x):
    n_particles = x.shape[0]
    j = [mlp_regress(x[i]) for i in range(n_particles)]
    return np.array(j)

#create bounds for hyperparamets

#min_bound_lr = 0.02
#max_bound_lr = 0.1
#boundslr = (min_bound_lr,max_bound_lr)

# Initialize swarm
#options = {'c1': 0.5 , 'c2': 0.3, 'w':0.9}
# Call instance of PSO
#bounds_lr = (np.array([0.01]),np.array([0.05]))
#dimensions = ( 1 )
#optimizer = ps.single.GlobalBestPSO(n_particles=20, dimensions=dimensions, options=options , bounds=bounds_lr)

# Perform optimization
#best_cost, pos = optimizer.optimize(f , iters=100)


alh = sp.ca(10, f , 0.5 , 1 , 1 ,5, mr=2, smp=2,
                 spc=False, cdc=1, srd=0.1, w=0.1, c=1.05, csi=0.6)

print ("best score :",alh.get_Gbest())