def fit(self, load_N=True, save_N=False, debug=False):
"""
load_N: load the precomputed N matrices if possible (it reduce the actual running time for sampling sketches)
save_N: save the computed N matrices for future use
"""
if self.solver_type == 'low_precision':
logger.info('Ready to start computing solutions!')
x, time = comp_sketch(self.matrix_Ab, 'x', load_N, save_N,
**self.params)
elif self.solver_type == 'high_precision':
num_iters = self.params.get('num_iters')
sketch_type = self.params.get('sketch_type')
# start computing a sketch
if sketch_type is not None:
N, time_proj = comp_sketch(self.matrix_Ab, 'N', load_N, save_N,
**self.params)
else:
N = [np.eye(self.matrix_Ab.n)]
self.k = 1
time_proj = 0
# start LSQR
time = [time_proj for i in range(num_iters)]
x = []
for i in range(self.k):
x_iter, y_iter, time_iter = lsqr_spark(self.matrix_Ab,
self.matrix_Ab.m,
self.matrix_Ab.n, N[i],
1e-10, num_iters)
x.append(x_iter)
time = [time[i] + time_iter[i] for i in range(num_iters)]
else:
raise ValueError("invalid solver_type")
self.x = x
self.time = time
if debug:
print self.x
def fit(self, load_N=True, save_N=False, debug=False):
"""
load_N: load the precomputed N matrices if possible (it reduce the actual running time for sampling sketches)
save_N: save the computed N matrices for future use
"""
if self.solver_type == 'low_precision':
logger.info('Ready to start computing solutions!')
x, time = comp_sketch(self.matrix_Ab, 'x', load_N, save_N, **self.params)
elif self.solver_type == 'high_precision':
num_iters = self.params.get('num_iters')
sketch_type = self.params.get('sketch_type')
# start computing a sketch
if sketch_type is not None:
N, time_proj = comp_sketch(self.matrix_Ab, 'N', load_N, save_N, **self.params)
else:
N = [np.eye(self.matrix_Ab.n-1)]
self.k = 1
time_proj = 0
b = []
# start lsqr
time = [time_proj for i in range(num_iters)]
x = []
for i in range(self.k):
x_iter, y_iter, time_iter = lsqr_spark(self.matrix_Ab,b,self.matrix_Ab.m,self.matrix_Ab.n-1,N[i],1e-10,num_iters)
x.append(x_iter)
time = [time[i] + time_iter[i] for i in range(num_iters)]
else:
raise ValueError("invalid solver_type")
self.x = x
self.time = time
if debug:
print self.x
def test_sketch_projection_x(self):
x, time = comp_sketch(self.matrix_Ab,
'x',
load_N=False,
save_N=False,
N_dire='N_file/',
sketch_type='projection',
projection_type='gaussian',
k=3,
c=1e2)
self.assertEqual(len(x), 3)
self.assertEqual(len(x[0]), self.matrix_Ab.n)
def test_sketch_projection_N(self):
N, time = comp_sketch(self.matrix_Ab,
'N',
load_N=False,
save_N=False,
N_dire='N_file/',
sketch_type='projection',
projection_type='gaussian',
k=3,
c=1e2)
self.assertEqual(len(N), 3)
self.assertEqual(N[0].shape, (self.matrix_Ab.n, self.matrix_Ab.n))
def test_sketch_sampling_x3(self):
x, time = comp_sketch(self.matrix_Ab,
'x',
load_N=True,
save_N=True,
N_dire='N_file/',
sketch_type='sampling',
projection_type='gaussian',
k=3,
c=1e2,
s=5e2)
self.assertEqual(len(x), 3)
self.assertEqual(len(x[0]), self.matrix_Ab.n)
def test_sketch_sampling_N2(self):
N, time = comp_sketch(self.matrix_Ab, 'N', load_N=True, save_N=False, N_dire='N_file/', sketch_type='projection', projection_type='gaussian', k=3, c=1e2, s=5e2)
self.assertEqual(len(N), 3)
self.assertEqual(N[0].shape, (self.matrix_Ab.n,self.matrix_Ab.n))
def test_sketch_sampling_x3(self):
x, time = comp_sketch(self.matrix_Ab, 'x', load_N=True, save_N=True, N_dire='N_file/', sketch_type='sampling', projection_type='gaussian', k=3, c=1e2, s=5e2)
self.assertEqual(len(x), 3)
self.assertEqual(len(x[0]), self.matrix_Ab.n)
def test_sketch_projection_x(self):
x, time = comp_sketch(self.matrix_Ab, 'x', load_N=False, save_N=False, N_dire='N_file/', sketch_type='projection', projection_type='gaussian', k=3, c=1e2)
self.assertEqual(len(x), 3)
self.assertEqual(len(x[0]), self.matrix_Ab.n)