def checkImageFormat(fmt,verbose=True):
"""Checks image format; if verbose, warn if it is not.
Returns the image format, or None if it is not OK.
"""
pf.debug("Format requested: %s" % fmt,pf.DEBUG.IMAGE)
pf.debug("Formats available: %s" % imageFormats(),pf.DEBUG.IMAGE)
if fmt in imageFormats():
if fmt == 'tex' and verbose:
pf.warning("This will only write a LaTeX fragment to include the 'eps' image\nYou have to create the .eps image file separately.\n")
return fmt
else:
if verbose:
import draw
draw.error("Sorry, can not save in %s format!\n"
"I suggest you use 'png' format ;)"%fmt)
return None
def checkImageFormat(fmt, verbose=True):
"""Checks image format; if verbose, warn if it is not.
Returns the image format, or None if it is not OK.
"""
pf.debug("Format requested: %s" % fmt, pf.DEBUG.IMAGE)
pf.debug("Formats available: %s" % imageFormats(), pf.DEBUG.IMAGE)
if fmt in imageFormats():
if fmt == 'tex' and verbose:
pf.warning(
"This will only write a LaTeX fragment to include the 'eps' image\nYou have to create the .eps image file separately.\n"
)
return fmt
else:
if verbose:
import draw
draw.error("Sorry, can not save in %s format!\n"
"I suggest you use 'png' format ;)" % fmt)
return None
"""
Q, R = randomized_range_finder(A, l=l)
return direct_svd(A, Q)
def algo_3(A, l=8, method='srft', verbose=False):
"""
Computes the third algorithm
"""
if verbose:
print "Computing Fast Randomized Range Finder"
Q, _ = fast_randomized_range_finder(A, l=l, method=method)
if verbose:
print "Compute direct SVD"
# return row_extraction_svd(A, Q)
return direct_svd(A, Q)
U1, S1, V1 = mem.cache(algo_1)(gaussian)
U2, S2, V2 = mem.cache(algo_2)(gaussian, l=100)
U3, S3, V3 = mem.cache(algo_3)(gaussian, l=100)
draw_plots([S_ground_truth[:100], S1[:100], S2[:100], S3[:100]],
legend=('Ground truth', 'Algo1', 'Algo2', 'Algo3'),
logscale=False)
draw_plots([error(S_ground_truth[:100], S1[:100]),
error(S_ground_truth[:100], S2[:100]),
error(S_ground_truth[:100], S3[:100])],
legend=('Error 1', 'Error 2', 'Error 3'))
pca = PCA(n_components=100)
pca.fit(data)
p0 = pca.explained_variance_
rpca = RandomizedPCA(n_components=100, iterated_power=1)
rpca.fit(data)
p1 = rpca.explained_variance_
rpca = RandomizedPCA(n_components=100, iterated_power=2)
rpca.fit(data)
p2 = rpca.explained_variance_
rpca = RandomizedPCA(n_components=100, iterated_power=3)
rpca.fit(data)
p3 = rpca.explained_variance_
draw_plots([gt[:100], e0[:100], e1[:100], e2[:100], e3[:100]],
legend=('grountruth', 'q=0', 'q=1', 'q=2', 'q=3'))
draw_plots([error(gt[:100], e0[:100]),
error(gt[:100], e1[:100]),
error(gt[:100], e2[:100]),
error(gt[:100], e3[:100])],
legend=('Error 1', 'Error 2', 'Error 3', 'Error 4'))
draw_plots([p0[:100], p1[:100], p2[:100], p3[:100]],
legend=('grountruth', 'q=0', 'q=1', 'q=2', 'q=3'))
draw_plots([error(p0[:100], p1[:100]),
error(p0[:100], p2[:100]),
error(p0[:100], p3[:100])],
legend=('Error 1', 'Error 2', 'Error 3'))
Returns
-------
U, S, V : SVD decomposition
"""
Q, R = randomized_range_finder(A, l=l)
return direct_svd(A, Q)
def algo_3(A, l=8, method='srft'):
"""
Computes the third algorithm
"""
Q, _ = fast_randomized_range_finder(A, l=l, method=method)
return direct_svd(A, Q)
U1, S1, V1 = mem.cache(algo_1)(gaussian)
U2, S2, V2 = mem.cache(algo_2)(gaussian, l=100)
U3, S3, V3 = mem.cache(algo_3)(gaussian, l=100)
draw_plots([S_ground_truth[:100], S1[:100], S2[:100], S3[:100]],
legend=('Ground truth', 'Algo1', 'Algo2', 'Algo3'))
draw_plots([
error(S_ground_truth[:100], S1[:100]),
error(S_ground_truth[:100], S2[:100]),
error(S_ground_truth[:100], S3[:100])
],
legend=('Error 1', 'Error 2', 'Error 3'))