def structured_product(self, x, OMEGA, OMEGA_CONJ):
y = np.copy(x)
#print(self._d_pr, self._d)
if self._d_pr <= self._d:
for block in self._BBB[::-1]:
# matrix-vector product
if block._hash_func == 'random':
y = np.dot(block._A, y)
if block._hash_func == 'circulant':
y = StructuredMatrices.circulant_product(block._col, y)
if block._hash_func == 'skew_circulant':
y = StructuredMatrices.skew_circulant_product(
block._col, y, OMEGA, OMEGA_CONJ)
if block._hash_func == 'toeplitz':
y = StructuredMatrices.toeplitz_product(
block._col, block._row, y)
if block._hash_func == 'hadamard':
y = StructuredMatrices.hadamard_cross_polytope_product(
y, block._D, block._nblocks)
if block._hash_func == 'hankel':
y = StructuredMatrices.hankel_product(
block._col, block._row, y)
if block._hash_func == 'low_displacement_rank':
y = StructuredMatrices.low_displacement_product(
block._G, block._H, block._r, y, OMEGA, OMEGA_CONJ)
if block._hash_func == 'kronecker':
y = StructuredMatrices.kronecker_product_rec(
block._A_list, y)
if block._hash_func == 'pure_hadamard':
#y = fht.fht1(y)
a = ffht.create_aligned(y.shape[0], np.float32)
np.copyto(a, y)
ffht.fht(a, min(y.shape[0], 1024, 2048))
y = a
if block._hash_func == 'diagonal' or block._hash_func == 'diagonal_kronecker' or block._hash_func == 'diagonal_gaussian':
y = block._D * y
#print(block, y.shape)
# all blocks have been handled.
# dimensionality reduction
y = y[:self._d_pr]
if self._normalization:
y *= self._norm_factor_list
return y
else:
if len(self._BBB) == 1 and self._BBB[0]._hash_func == 'random':
y = np.dot(self._BBB[0]._A, y)
return y
y_global = np.zeros(self._d_pr)
div = self._d_pr // self._d # number of parameters
div_real = self._d_pr / self._d
#print(div, div_real, self._d_pr, self._d)
assert div == div_real # we handle only this case
# /!\ + d_pr should be an integral power of 2!!!
for i_div in xrange(div):
#print('i_div', i_div)
y = np.copy(x)
# if div_real != div, the last mini block should be truncated => never happened because of the case of hadamard
# split y into small vectors and reunit all results after
for block in self._BBB[::-1]:
#if i_div == div or i_div < div -1:
# easy
# matrix-vector product
if block._hash_func == 'circulant':
y = StructuredMatrices.circulant_product(
block._col[i_div], y)
if block._hash_func == 'skew_circulant':
y = StructuredMatrices.skew_circulant_product(
block._col[i_div], y, OMEGA, OMEGA_CONJ)
if block._hash_func == 'toeplitz':
y = StructuredMatrices.toeplitz_product(
block._col[i_div], block._row[i_div], y)
if block._hash_func == 'hadamard':
y = StructuredMatrices.hadamard_cross_polytope_product(
y, block._D[i_div], block._nblocks)
if block._hash_func == 'hankel':
y = StructuredMatrices.hankel_product(
block._col[i_div], block._row[i_div], y)
if block._hash_func == 'low_displacement_rank':
y = StructuredMatrices.low_displacement_product(
block._G[i_div], block._H[i_div], block._r, y,
OMEGA, OMEGA_CONJ)
if block._hash_func == 'kronecker':
y = StructuredMatrices.kronecker_product_rec(
block._A_list[i_div], y)
if block._hash_func == 'pure_hadamard':
a = ffht.create_aligned(y.shape[0], np.float32)
np.copyto(a, y)
ffht.fht(a, min(x.shape[0], 1024, 2048))
y = a
if block._hash_func == 'diagonal' or block._hash_func == 'diagonal_kronecker' or block._hash_func == 'diagonal_gaussian':
y = block._D[i_div] * y
y_global[i_div * self._d:i_div * self._d + self._d] = y
#print(y_global)
if self._normalization:
y_global *= self._norm_factor_list
return y_global
import numpy as np
import ffht
import timeit
reps = 1000
n = 2**20
chunk_size = 1024
a = ffht.create_aligned(n, np.float32)
np.copyto(a, np.random.randn(n))
t1 = timeit.default_timer()
for i in range(reps):
ffht.fht(a, chunk_size)
t2 = timeit.default_timer()
print(t2 - t1 + 0.0) / (reps + 0.0)
import numpy as np
import ffht
import timeit
reps = 1000
n = 2**20
chunk_size = 1024
a = ffht.create_aligned(n, np.float32)
np.copyto(a, np.random.randn(n))
t1 = timeit.default_timer()
for i in range(reps):
ffht.fht(a, chunk_size)
t2 = timeit.default_timer()
print (t2 - t1 + 0.0) / (reps + 0.0)