# -*- coding: utf-8 -*-
"""
Created on Tue Mar 6 19:30:05 2018
@author: Erik
"""
import get_data as gd
import part2b_code as p2b
import part1c_code as p1c
import gradient_calculation as gc
X_test, y_test = gd.read_data_formatted('test_struct.txt')
X_train, y_train = gd.read_data_formatted('train_struct_1000.txt')
params = gd.get_params()
p2b.optimize(params, X_train, y_train, 1000, 'solution_1000_distortion')
params = p2b.get_optimal_params('solution_1000_distortion')
w = gc.w_matrix(params)
t = gc.t_matrix(params)
print("Function value: ")
print(p2b.func_to_minimize(params, X_train, y_train, 1000))
y_pred = p2b.predict(X_test, w, t)
print(p2b.accuracy(y_pred, y_test))
# -*- coding: utf-8 -*-
"""
Created on Sat Mar 3 14:11:12 2018
@author: Erik
"""
import gradient_calculation as gc
import get_data as gd
import numpy as np
from scipy.optimize import check_grad
import part2b_code as p2b
import time
params = gd.get_params()
X, y = gd.read_data_formatted('train_struct.txt')
X = X[0:100]
y = y[0:100]
start = time.time()
print(check_grad(p2b.func_to_minimize, p2b.grad_func, params, X, y, 10))
print("Finished in " + str(time.time() - start) + " seconds")
