def test_projection_SRDHT_x(self):
proj = Projections(projection_type='srdht',sc=sc,c=1e2)
sol = proj.execute(self.matrix_Ab, 'x')
self.assertEqual(len(sol[0]), self.matrix_Ab.n)
def test_projection_Gaussian_N(self):
proj = Projections(projection_type='gaussian',sc=sc,c=1e2,k=3)
N = proj.execute(self.matrix_Ab, 'N')
self.assertEqual(len(N), 3)
self.assertEqual(N[0].shape, (self.matrix_Ab.n,self.matrix_Ab.n))
def test_projection_Gaussian_x(self):
proj = Projections(projection_type='gaussian',sc=sc,c=1e2,k=3)
sol = proj.execute(self.matrix_Ab, 'x')
self.assertEqual(len(sol), 3)
self.assertEqual(len(sol[0]), self.matrix_Ab.n)
def test_projection_Rademacher_x(self):
proj = Projections(projection_type='rademacher',sc=sc,c=1e2)
sol = proj.execute(self.matrix_Ab, 'x')
self.assertEqual(len(sol[0]), self.matrix_Ab.n)
def comp_sketch(matrix, objective, load_N=False, save_N=False, N_dir='../N_file/', **kwargs):
"""
Given matrix A, the function comp_sketch computes a sketch for A and performs further operations on PA.
It returns the total running time and the desired quantity.
parameter:
matrix: a RowMatrix object storing the matrix [A b]
objective: either 'x' or 'N'
'x': the function returns the solution to the problem min_x || PA[:,:-1]x - PA[:,-1] ||_2
'N': the function returns a square matrix N such that PA[:,:-1]*inv(N) is a matrix with orthonormal columns
load_N: load the precomputed N matrices if possible (it reduces the actual running time for sampling sketches)
save_N: save the computed N matrices for future use
sketch_type: either 'projection' or 'sampling'
projection_type: cw, gaussian, rademacher or srdht
c: projection size
s: sampling size (for sampling sketch only)
k: number of independent trials to run
"""
sketch_type = kwargs.get('sketch_type')
if not os.path.exists(N_dir):
os.makedirs(N_dir)
if objective == 'x':
if sketch_type == 'projection':
projection = Projections(**kwargs)
t = time.time()
x = projection.execute(matrix, 'x', save_N)
t = time.time() - t
if save_N:
logger.info('Saving N matrices from projections!')
N = [a[0] for a in x]
x = [a[1] for a in x]
# saving N
filename = N_dir + 'N_' + matrix.name + '_projection_' + kwargs.get('projection_type') + '_c' + str(int(kwargs.get('c'))) + '_k' + str(int(kwargs.get('k')))+ '.dat'
data = {'N': N, 'time': t}
pickle_write(filename,data)
elif sketch_type == 'sampling':
s = kwargs.get('s')
new_N_proj = 0
N_proj_filename = N_dir + 'N_' + matrix.name + '_projection_' + kwargs.get('projection_type') + '_c' + str(int(kwargs.get('c'))) + '_k' + str(int(kwargs.get('k'))) +'.dat'
if load_N and os.path.isfile(N_proj_filename):
logger.info('Found N matrices from projections, loading them!')
N_proj_filename = N_dir + 'N_' + matrix.name + '_projection_' + kwargs.get('projection_type') + '_c' + str(int(kwargs.get('c'))) + '_k' + str(int(kwargs.get('k'))) +'.dat'
result = pickle_load(N_proj_filename)
N_proj = result['N']
t_proj = result['time']
else: # otherwise, compute it
t = time.time()
projection = Projections(**kwargs)
N_proj = projection.execute(matrix, 'N')
t_proj = time.time() - t
new_N_proj = 1
sampling = Sampling(N=N_proj)
t = time.time()
x = sampling.execute(matrix, 'x', s, save_N )
t = time.time() - t + t_proj
if save_N and new_N_proj:
logger.info('Saving N matrices from projections!')
#filename = N_dir + 'N_' + matrix.name + '_projection_' + kwargs.get('projection_type') + '_c' + str(int(kwargs.get('c'))) + '_k' + str(int(kwargs.get('k'))) + '.dat'
data = {'N': N_proj, 'time': t_proj}
pickle_write(N_proj_filename,data)
if save_N:
logger.info('Saving N matrices from sampling!')
N = [a[0] for a in x]
x = [a[1] for a in x]
filename = N_dir + 'N_' + matrix.name + '_sampling_s' + str(int(kwargs.get('s'))) + '_' + kwargs.get('projection_type') + '_c' + str(int(kwargs.get('c'))) + '_k' + str(int(kwargs.get('k'))) + '.dat'
data = {'N': N, 'time': t}
pickle_write(filename,data)
else:
raise ValueError('Please enter a valid sketch type!')
return x, t
elif objective == 'N':
if sketch_type == 'projection':
N_proj_filename = N_dir + 'N_' + matrix.name + '_projection_' + kwargs.get('projection_type') + '_c' + str(int(kwargs.get('c'))) + '_k' + str(int(kwargs.get('k'))) + '.dat'
if load_N and os.path.isfile(N_proj_filename):
logger.info('Found N matrices from projections, loading them!')
result = pickle_load(N_proj_filename)
N = result['N']
t = result['time']
else:
t = time.time()
projection = Projections(**kwargs)
N = projection.execute(matrix, 'N')
t = time.time() - t
if save_N:
logger.info('Saving N matrices from projections!')
data = {'N': N, 'time': t}
pickle_write(N_proj_filename,data)
elif sketch_type == 'sampling':
s = kwargs.get('s')
new_N_proj = 0
new_N_samp = 0
N_samp_filename = N_dir + 'N_' + matrix.name + '_sampling_s' + str(int(kwargs.get('s'))) + '_' + kwargs.get('projection_type') + '_c' + str(int(kwargs.get('c'))) + '_k' + str(int(kwargs.get('k'))) + '.dat'
N_proj_filename = N_dir + 'N_' + matrix.name + '_projection_' + kwargs.get('projection_type') + '_c' + str(int(kwargs.get('c'))) + '_k' + str(int(kwargs.get('k'))) + '.dat'
if load_N and os.path.isfile(N_samp_filename):
logger.info('Found N matrices from sampling, loading them!')
result = pickle_load(N_samp_filename)
N = result['N']
t = result['time']
elif load_N and os.path.isfile(N_proj_filename):
logger.info('Found N matrices from projections, loading them!')
result = pickle_load(N_proj_filename)
N_proj = result['N']
t_proj = result['time']
sampling = Sampling(N=N_proj)
t = time.time()
N = sampling.execute(matrix, 'N', s)
t = time.time() - t + t_proj
new_N_samp = 1
else:
t = time.time()
projection = Projections(**kwargs)
N_proj = projection.execute(matrix, 'N')
t_proj = time.time() - t
new_N_proj = 1
t = time.time()
sampling = Sampling(N=N_proj)
N = sampling.execute(matrix, 'N', s)
t = time.time() - t + t_proj
new_N_samp = 1
if save_N and new_N_proj:
logger.info('Saving N matrices from projections!')
data = {'N': N_proj, 'time': t_proj}
pickle_write(N_proj_filename,data)
if save_N and new_N_samp:
logger.info('Saving N matrices from sampling!')
data = {'N': N, 'time': t}
pickle_write(N_samp_filename,data)
else:
raise ValueError('Please enter a valid sketch type!')
return N, t
else:
raise ValueError('Please enter a valid objective!')
def test_projection_Gaussian_N(self):
proj = Projections(projection_type='gaussian', sc=sc, c=1e2, k=3)
N = proj.execute(self.matrix_Ab, 'N')
self.assertEqual(len(N), 3)
self.assertEqual(N[0].shape, (self.matrix_Ab.n, self.matrix_Ab.n))
def test_projection_SRDHT_x(self):
proj = Projections(projection_type='srdht', sc=sc, c=1e2)
sol = proj.execute(self.matrix_Ab, 'x')
self.assertEqual(len(sol[0]), self.matrix_Ab.n)
def test_projection_Rademacher_x(self):
proj = Projections(projection_type='rademacher', sc=sc, c=1e2)
sol = proj.execute(self.matrix_Ab, 'x')
self.assertEqual(len(sol[0]), self.matrix_Ab.n)
def test_projection_Gaussian_x(self):
proj = Projections(projection_type='gaussian', sc=sc, c=1e2, k=3)
sol = proj.execute(self.matrix_Ab, 'x')
self.assertEqual(len(sol), 3)
self.assertEqual(len(sol[0]), self.matrix_Ab.n)
