from code import utils, architecture_1, architecture_2, load_data import code.histograms as h train, train_cleaned, test = load_data.load_data() h.plot_train_hist(train, train_cleaned) h.train_clean_diff(train, train_cleaned) h.get_correlation(train_cleaned, 0) h.obtain_freq(train, .75) h.data_threshold(train, .2474) hist_pred = h.predict(train, .749) arch1 = architecture_1.model() arch1.fit(train, train_cleaned) arch1_pred = arch1.predict(train) utils.display_prediction([train, train_cleaned, hist_pred, arch1_pred], cols=2, labels=['ori', 'cleaned', 'hist', 'conv'], index=0)
from nose.tools import assert_equal, assert_true, assert_raises, assert_almost_equal
from code import architectures, load_data
from numpy.testing import assert_array_almost_equal
import pdb
train = load_data.load_data('train', max_images=2)
cleaned = load_data.load_data('train_cleaned', max_images=2)
avg = load_data.load_data('train_avg', max_images=2)
def setup():
print("SETUP!")
def teardown():
print("TEAR DOWN!")
def test_basic():
print("I RAN!")
def model1():
model = architectures.model(1, [3], [2], 't0')
model.fit(train, cleaned, 1)
def model2():
model = architectures.model(2, [1,3], [3, 3], 't1')
model.fit(train, cleaned, 1)
def model3():
model = architectures.model(3, [1,3], [3, 3], 't1')
model.fit(train, cleaned, 1, X2=avg)
