def fit_hopfield(params):
# get params
n_label = params.get('n_label', None)
n_sample = params['n_sample']
fit_mode = params['fit_mode']
# load dataset
dataset = load_alphabet()
# target_name
target_names = params.get('target_names', None)
if target_names is None:
target_names = dataset.target_names[:n_label]
# transform data
dataset.data = binarize(dataset.data, binary_values=(-1,1))
# create train data
X, y = create_train_data(data=dataset.data,
target=dataset.target,
target_names=target_names,
n_sample=n_sample)
print_train_data(X, y, target_names)
# fit hopfield
hf = Hopfield(mode=fit_mode)
hf.fit(X, y, watch_weight=False)
# set params
params['img_shape'] = dataset.image_shape
return hf, X, y, target_names, params
def test_load_alphabet():
dataset = load_alphabet()
# number of labels
assert len(np.unique(dataset.target)) == len(dataset.target_names)
# number of data
assert len(dataset.data) == len(dataset.target)
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import matplotlib.pyplot as plt
from data import load_alphabet
dataset = load_alphabet()
images = []
for t in dataset.target_names:
images.append(dataset.images[dataset.target == t][0])
for i, (label, img) in enumerate(zip(dataset.target_names, images)):
plt.subplot(2, len(dataset.target_names) / 2, i + 1)
plt.imshow(img, cmap="gray")
plt.title(label.upper())
plt.axis("off")
plt.savefig("alphabet_images.png")