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from __future__ import print_function
import numpy as np
np.random.seed(0)
from numpy.testing import assert_allclose
from hypothesis import given, assume
from hypothesis.strategies import tuples, lists, integers, floats
from hypothesis.extra.numpy import arrays
def lengths(lo=1, hi=10):
return integers(min_value=lo, max_value=hi)
def shapes(min_rows=1, max_rows=100, min_cols=1, max_cols=100):
return tuples(lengths(lo=min_rows, hi=max_rows), lengths(lo=min_cols, hi=max_cols))
def ndarrays_of_shape(shape, lo=-1000.0, hi=1000.0, dtype='float64'):
return arrays(dtype, shape=shape, elements=floats(min_value=lo, max_value=hi, width=32))
def ndarrays(min_len=0, max_len=10, min_val=-10000000.0, max_val=1000000.0, dtype='float64'):
return lengths(lo=min_len, hi=max_len).flatmap(
lambda n: ndarrays_of_shape(n, lo=min_val, hi=max_val, dtype=dtype))
def matrices(min_rows=1, max_rows=10, min_cols=1, max_cols=10,
min_value=-10000000.0, max_value=1000000.0, dtype='float64'):
return shapes(min_rows=min_rows, max_rows=max_rows,
min_cols=min_cols, max_cols=max_cols).flatmap(
lambda mn: ndarrays_of_shape(mn, lo=min_value, hi=max_value,
dtype=dtype))
def positive_ndarrays(min_len=0, max_len=10, max_val=100000.0, dtype='float64'):
return ndarrays(min_len=min_len, max_len=max_len, min_val=0, max_val=max_val,
dtype=dtype)
def negative_ndarrays(min_len=0, max_len=10, min_val=-100000.0, dtype='float64'):
return ndarrays(min_len=min_len, max_len=max_len, min_val=min_val, max_val=-1e-10,
dtype=dtype)
def parse_layer(layer_data):
# Get the first row, excluding the first column
x = layer_data[0,1:]
# Get the first column, excluding the first row
# .ascontiguousarray is support important here!!!!
b = np.ascontiguousarray(layer_data[1:,0], dtype='float64')
# Slice out the row and the column used for the X and the bias
W = layer_data[1:,1:]
assert x.ndim == 1
assert b.ndim == 1
assert b.shape[0] == W.shape[0]
assert x.shape[0] == W.shape[1]
assume(not np.isnan(W.sum()))
assume(not np.isnan(x.sum()))
assume(not np.isnan(b.sum()))
assume(not any(np.isinf(val) for val in W.flatten()))
assume(not any(np.isinf(val) for val in x))
assume(not any(np.isinf(val) for val in b))
return x, b, W
def split_row(layer_data):
return (layer_data[0,:], layer_data[:,:])
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