def test_mnist_build_a_dichotomy():
mnist = make_dataset()
mnist_parity = [
list(map(lambda x: 2 * x, range(4))),
list(map(lambda x: 2 * x + 1, range(4)))
]
mnist.build_dichLabels(mnist_parity, 'parity')
mnist_smallness = [range(0, 4), range(4, 8)]
mnist.build_dichLabels(mnist_smallness, 'smaller_than_4')
assert True
def test_mnist_hstack_dichotomies():
mnist = make_dataset()
# Currently dichotomies will only be binary
mnist_parity = [
list(map(lambda x: 2 * x, range(4))),
list(map(lambda x: 2 * x + 1, range(4)))
]
mnist_smallness = [range(0, 4), range(4, 8)]
mnist.build_dichLabels(mnist_smallness, 'smaller_than_4')
mnist.build_dichLabels(mnist_parity, 'parity')
mnist.hstack_dichs('parity', 'smaller_than_4')
assert True
def test_mnist_product_dichotomies():
mnist = make_dataset()
# Currently dichotomies will only be binary
mnist_parity = [
list(map(lambda x: 2 * x, range(4))),
list(map(lambda x: 2 * x + 1, range(4)))
]
mnist_smallness = [range(0, 4), range(4, 8)]
mnist_prod = [
set(s1).intersection(set(s2)) for s2 in mnist_smallness
for s1 in mnist_parity
]
mnist.build_dichLabels(mnist_smallness, 'smaller_than_4')
mnist.build_dichLabels(mnist_parity, 'parity')
mnist.hstack_dichs('parity', 'smaller_than_4')
assert True
def build_mnist_ds(filt_lbls=range(8), spl=0.04):
from keras.datasets import mnist
from data_tools import ImageDataset
(x_train, y_train), (x_test, y_test) = mnist.load_data()
filt_labels = range(8)
mnist = ImageDataset(x_train,
y_train,
x_test,
y_test,
filt_labels=filt_lbls,
spl=spl)
mnist_parity = [
list(map(lambda x: 2 * x, range(4))),
list(map(lambda x: 2 * x + 1, range(4)))
]
mnist_smallness = [range(0, 4), range(4, 8)]
mnist_prod = [
set(s1).intersection(set(s2)) for s2 in mnist_smallness
for s1 in mnist_parity
]
mnist.build_dichLabels(mnist_smallness, 'smaller_than_4')
mnist.build_dichLabels(mnist_parity, 'parity')
mnist.hstack_dichs('parity', 'smaller_than_4')
mnist.compstack_dichs('parity', 'smaller_than_4')
mnist.build_dichLabels(mnist_prod, 'parity_prod_smaller_than_4')
return mnist