rowSampler.RowSampler,\
randomProjections.RandomProjections,\
randomSums.RandomSums,\
frequentDirections.FrequentDirections]
ns = [500]
ds = [100]
ells = range(10,101,10)
ks = [5]
rounds = 1
for (n, d, k) in product (ns, ds, ks):
data_maker = SyntheticDataMaker()
data_maker.initBeforeMake(d, k, signal_to_noise_ratio=10.0)
A = data_maker.makeMatrix(n) # n * d matrix
ATA = covariance(A.T)
squared_frob_A = norm(A,'fro') ** 2
A_rank_k = truncateSVD(A, k)
for (sketcherClass, ell, r) in product(sketcherClasses, ells, range(rounds)):
if ell > d/2:
continue
sketcher = sketcherClass(d, ell)
for row in A:
sketcher.append(row)
sketch = sketcher.get()
diff = ATA - dot(sketch.transpose(),sketch)
relative_cov_err = norm(diff,2) / squared_frob_A
if __name__ == '__main__':
# make input data
n = 1000
d = 400
k = 5
ells = range(10,21,10)
density = 0.1
A = rand(n, d, density, format = 'lil')
# error computation
B = A.todense()
ATA = covariance(B.T)
squared_frob_A = norm(B,'fro') ** 2
A_rank_k = truncateSVD(B, k)
opt_rank_k_err = norm(B - A_rank_k, 'fro') ** 2
for ell in ells:
sketcher = SparseSketcher(d, ell)
t_start = timer()
for sv in A:
sketcher.append(sv)
t_end = timer()
totalSketchTime = t_end-t_start
sketch = sketcher.get()
rowSampler.RowSampler,\
randomProjections.RandomProjections,\
randomSums.RandomSums,\
frequentDirections.FrequentDirections]
ns = [500]
ds = [100]
ells = range(10, 101, 10)
ks = [5]
rounds = 1
for (n, d, k) in product(ns, ds, ks):
data_maker = SyntheticDataMaker()
data_maker.initBeforeMake(d, k, signal_to_noise_ratio=10.0)
A = data_maker.makeMatrix(n) # n * d matrix
ATA = covariance(A.T)
squared_frob_A = norm(A, 'fro')**2
A_rank_k = truncateSVD(A, k)
for (sketcherClass, ell, r) in product(sketcherClasses, ells,
range(rounds)):
if ell > d / 2:
continue
sketcher = sketcherClass(d, ell)
for row in A:
sketcher.append(row)
sketch = sketcher.get()
diff = ATA - dot(sketch.transpose(), sketch)
self.__rotate__()
return self._sketch[:self.ell, :]
if __name__ == '__main__':
# make input data
n = 1000
d = 400
k = 5
ells = range(10, 21, 10)
density = 0.1
A = rand(n, d, density, format='lil')
# error computation
B = A.todense()
ATA = covariance(B.T)
squared_frob_A = norm(B, 'fro')**2
A_rank_k = truncateSVD(B, k)
opt_rank_k_err = norm(B - A_rank_k, 'fro')**2
for ell in ells:
sketcher = SparseSketcher(d, ell)
t_start = timer()
for sv in A:
sketcher.append(sv)
t_end = timer()
totalSketchTime = t_end - t_start
sketch = sketcher.get()
#### cov-error #######