# -*- 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))
Esempio n. 2
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# -*- 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")