def random_mutation(population, m_probability, m_method):
SEQUENCE = cupy.random.uniform(0, 1, population.shape[0])
mutated_population, mutated_chromosome = None, None
for i in range(population.shape[0]):
chromosome = population[i]
if SEQUENCE[i] < m_probability:
# mutate chromosome
if m_method[1] == 'gauss':
mutated_chromosome = gauss_replacement(chromosome)
elif m_method[1] == 'reset':
mutated_chromosome = reset_replacement(chromosome)
else:
print(
'\nError [8]: Invalid mutation method combination [at random mutation]\nExiting...'
)
exit()
# append chromosomes to the mutated population
if i == 0:
# if loop run for first time, then initialize the generation population
mutated_population = mutated_chromosome
else:
# after first time, stack chromosomes to the generation population
mutated_population = cupy.vstack(
(mutated_population, mutated_chromosome))
else:
# NO mutation
# append chromosomes to the mutated population
if i == 0:
# if loop run for first time, then initialize the generation population
mutated_population = chromosome
else:
# after first time, stack chromosomes to the generation population
mutated_population = cupy.vstack(
(mutated_population, chromosome))
return mutated_population
def remove_indices(self):
"""make feature vector `self.features`"""
# 0<=v,d1,d2,d3<=28, 0<=e<=28*28=784, so v,d1,d2,d3 has 10**2 spaces and e has 10**3 spaces.
if not self.use_d:
features = cp.vstack((self.num_vertices, self.num_edges))
features = features[0] + features[1] * (10**2) # ve
else:
features = cp.vstack((self.num_vertices, self.num_edges,
self.num_id1, self.num_id2, self.num_id3))
features = features[0] + features[1] * (10**2) + features[2] * (
10**
(2 + 3)) + features[3] * (10**(2 + 3 + 2)) + features[4] * (
10**(2 + 3 + 2 + 2)) # veid1id2id3
self.features = cp.unique(features, return_counts=True)
self.prob_of_measuring_0ket = cp.sum(
self.features[1]**2) / (2**(2 * self.adjacency_mat.shape[0]))
divide_value = np.sqrt(cp.sum(self.features[1]**2))
self.normalized_features = (self.features[0],
self.features[1] / divide_value)
del self.indices
del self.num_vertices
del self.num_edges
if self.use_d:
del self.num_id1
del self.num_id2
del self.num_id3
self.features = (cp.asnumpy(self.features[0]),
cp.asnumpy(self.features[1]))
self.normalized_features = (cp.asnumpy(self.normalized_features[0]),
cp.asnumpy(self.normalized_features[1]))
def inverse_bwt(string, ind):
# Перевод строки в список чисел по юникоду
str_list = [ord(i) for i in string]
# Переход к массиву на GPU
# Массив с последовательностью введенной строки
s_arr = cp.array(str_list)
# отсортированная последовательность
sorted_s = cp.array(sorted(str_list))
# Просто слияние двух предыдущих массивов
tab_s = cp.vstack((s_arr, sorted_s))
for i in range(1, len(s_arr) - 1):
# Сортировка, с получением индексов
# массива tab_s для новой строки, хотя фактически - столбца
# (метод .T меняет оси массива местами)
j = cp.lexsort(
cp.array([tab_s.T[:, i].tolist() for i in range(i, -1, -1)]))
# Добавление отсортированной по j строки к массиву
tab_s = cp.vstack((s_arr, tab_s.T[j].T))
# Сортировка последней строки
j = cp.lexsort(
cp.array(
[tab_s.T[:, i].tolist() for i in range(len(s_arr) - 1, -1, -1)]))
# Обратный перевод чисел в симвлы по юникоду
str_list = [chr(i) for i in tab_s.T[j][ind]]
return ''.join(str_list)
def uniform_crossover(parent_pairs, x_probability):
# define random chromosome probability sequence (to decide randomly whether ot perform crossover or not)
X_SEQUENCE = cupy.random.uniform(0, 1, parent_pairs.shape[0])
# define random element probability sequence (for uniform crossover)
U_SEQUENCE = cupy.random.uniform(
0, 1, parent_pairs.shape[0] * parent_pairs.shape[1]).reshape(
parent_pairs.shape[0], parent_pairs.shape[1])
# define element probability crossover (for uniform crossover)
CONST_PROB = 0.5
# define new generation population variable
population_hat = None
# perform single point crossover
for i in range(parent_pairs.shape[0]):
X, Y = parent_pairs[i]
# check chromosomes' compatibility in case there is an error
compatible_chromosomes = X.shape == Y.shape
# define max index boundary
chromosome_shape = X.shape[0]
# initialize new chromosome
a, b = cupy.zeros((2, chromosome_shape), dtype=cupy.int64)
if not compatible_chromosomes:
print(
"Error [14]: Incompatible chromosomes (at: multiple point selection)\nExiting...)"
)
exit()
else:
# crossover with respect to the crossover probability
if X_SEQUENCE[i] < x_probability:
# create children
for c, (k, l) in enumerate(zip(X, Y)):
# repeatedly toss a coin and check with respect to CONST_PROB
if CONST_PROB > U_SEQUENCE[i][c]:
# gene exchange
a[c] = l
b[c] = k
else:
# NO gene exchange
a[c] = k
b[c] = l
# append children to form the new population
if i == 0:
# if loop run for first time, then initialize the generation population
population_hat = cupy.stack((a, b))
else:
# after first time, stack chromosomes to the generation population
population_hat = cupy.vstack(
(population_hat, cupy.stack((a, b))))
else:
# append parents to the new population
if i == 0:
# if loop run for first time, then initialize the generation population
population_hat = cupy.stack((X, Y))
else:
# after first time, stack chromosomes to the generation population
population_hat = cupy.vstack(
(population_hat, cupy.stack((X, Y))))
return population_hat
def get_lidar_data(self, input_points: dict):
print("get one frame lidar data.")
self.current_frame["lidar_stamp"] = input_points['stamp']
self.current_frame["lidar_seq"] = input_points['seq']
self.current_frame["points"] = input_points['points'].T
self.lidar_deque.append(deepcopy(self.current_frame))
if len(self.lidar_deque) == 5:
ref_from_car = self.imu2lidar
car_from_global = transform_matrix(self.lidar_deque[-1]['translation'], self.lidar_deque[-1]['rotation'], inverse=True)
ref_from_car_gpu = cp.asarray(ref_from_car)
car_from_global_gpu = cp.asarray(car_from_global)
for i in range(len(self.lidar_deque) - 1):
last_pc = self.lidar_deque[i]['points']
last_pc_gpu = cp.asarray(last_pc)
global_from_car = transform_matrix(self.lidar_deque[i]['translation'], self.lidar_deque[i]['rotation'], inverse=False)
car_from_current = self.lidar2imu
global_from_car_gpu = cp.asarray(global_from_car)
car_from_current_gpu = cp.asarray(car_from_current)
transform = reduce(
cp.dot,
[ref_from_car_gpu, car_from_global_gpu, global_from_car_gpu, car_from_current_gpu],
)
# tmp_1 = cp.dot(global_from_car_gpu, car_from_current_gpu)
# tmp_2 = cp.dot(car_from_global_gpu, tmp_1)
# transform = cp.dot(ref_from_car_gpu, tmp_2)
last_pc_gpu = cp.vstack((last_pc_gpu[:3, :], cp.ones(last_pc_gpu.shape[1])))
last_pc_gpu = cp.dot(transform, last_pc_gpu)
self.pc_list.append(last_pc_gpu[:3, :])
current_pc = self.lidar_deque[-1]['points']
current_pc_gpu = cp.asarray(current_pc)
self.pc_list.append(current_pc_gpu[:3,:])
all_pc = np.zeros((5, 0), dtype=float)
for i in range(len(self.pc_list)):
tmp_pc = cp.vstack((self.pc_list[i], cp.zeros((2, self.pc_list[i].shape[1]))))
tmp_pc = cp.asnumpy(tmp_pc)
ref_timestamp = self.lidar_deque[-1]['lidar_stamp'].to_sec()
timestamp = self.lidar_deque[i]['lidar_stamp'].to_sec()
tmp_pc[3, ...] = self.lidar_deque[i]['points'][3, ...]
tmp_pc[4, ...] = ref_timestamp - timestamp
all_pc = np.hstack((all_pc, tmp_pc))
all_pc = all_pc.T
print(f" concate pointcloud shape: {all_pc.shape}")
self.points = all_pc
sync_cloud = xyz_array_to_pointcloud2(all_pc[:, :3], stamp=self.lidar_deque[-1]["lidar_stamp"], frame_id="lidar_top")
pub_sync_cloud.publish(sync_cloud)
return True
def add_new_objs(self, new_obj_n):
x = cp.random.rand(new_obj_n) * (self.x_max - self.x_min) + self.x_min
y = cp.random.rand(new_obj_n) * (self.y_max - self.y_min) + self.y_min
vx = (-1)**cp.random.randint(
1, 3, new_obj_n) * cp.random.rand(new_obj_n) * self.v_abs
vy = (-1)**cp.random.randint(1, 3,
new_obj_n) * cp.sqrt(new_obj_n**2 - vx**2)
new_obj_state = cp.vstack((x, y, vx, vy)).T # (new_obj_n, 4)
self.state = cp.vstack((self.state, new_obj_state))
def movepixels_3d(self, Iin, Tx):
'''
This function will translate the pixels of an image
according to Tx translation images.
Inputs;
Tx: The transformation image, dscribing the
(backwards) translation of every pixel in the x direction.
Outputs,
Iout : The transformed image
'''
nx, ny, nz = Iin.shape
tmpx = cp.linspace(0, nx - 1, num=nx, dtype=np.float32)
tmpy = cp.linspace(0, ny - 1, num=ny, dtype=np.float32)
tmpz = cp.linspace(0, nz - 1, num=nz, dtype=np.float32)
x, y, z = cp.meshgrid(tmpx, tmpy, tmpz, indexing='ij')
Tlocalx = cp.expand_dims(x + Tx, axis=0)
y = cp.expand_dims(y, axis=0)
z = cp.expand_dims(z, axis=0)
coordinates = cp.vstack((Tlocalx, y, z))
return map_coordinates(Iin, coordinates, order=1, cval=0)
def read_dense_as_cupy_csr(dataset, axis_chunk=6000):
sub_matrices = []
for idx in idx_chunks_along_axis(dataset.shape, 0, axis_chunk):
dense_chunk = dataset[idx]
sub_matrix = cupy.sparse.csr_matrix(dense_chunk)
sub_matrices.append(sub_matrix)
return cupy.vstack(sub_matrices, format="csr")
def calculate_snrs(self, interferometer, waveform_polarizations):
name = interferometer.name
signal_ifo = xp.sum(
xp.vstack([
waveform_polarizations[mode] * float(
interferometer.antenna_response(
self.parameters["ra"],
self.parameters["dec"],
self.parameters["geocent_time"],
self.parameters["psi"],
mode,
)) for mode in waveform_polarizations
]),
axis=0,
)[interferometer.frequency_mask]
time_delay = (self.parameters["geocent_time"] -
interferometer.strain_data.start_time +
interferometer.time_delay_from_geocenter(
self.parameters["ra"],
self.parameters["dec"],
self.parameters["geocent_time"],
))
signal_ifo *= xp.exp(-2j * np.pi * time_delay * self.frequency_array)
d_inner_h = xp.sum(
xp.conj(signal_ifo) * self.strain[name] / self.psds[name])
h_inner_h = xp.sum(xp.abs(signal_ifo)**2 / self.psds[name])
return d_inner_h, h_inner_h
def multiple_point_crossover(parent_pairs, x_probability):
# define random probability sequence
SEQUENCE = cupy.random.uniform(0, 1, parent_pairs.shape[0])
# define new generation population variable
population_hat = None
# perform single point crossover
for i in range(parent_pairs.shape[0]):
X, Y = parent_pairs[i]
# check chromosomes' compatibility in case there is an error
compatible_chromosomes = X.shape == Y.shape
# define max index boundary
chromosome_shape = X.shape[0]
# initialize new chromosome
a, b = cupy.zeros((2, chromosome_shape), dtype=cupy.int64)
if not compatible_chromosomes:
print(
"Error [13]: Incompatible chromosomes (at: multiple point selection\nExiting...)"
)
exit()
else:
# crossover random point
x_idx, y_idx = cupy.sort(
cupy.random.randint(0, chromosome_shape, 2))
# first child chromosome
a = cupy.concatenate((X[:x_idx], Y[x_idx:y_idx], X[y_idx:]))
# second child chromosome
b = cupy.concatenate((Y[:x_idx], X[x_idx:y_idx], Y[y_idx:]))
# crossover with respect to the crossover probability
if SEQUENCE[i] < x_probability:
# append children to form the new population
if i == 0:
# if loop run for first time, then initialize the generation population
population_hat = cupy.stack((a, b))
else:
# after first time, stack chromosomes to the generation population
population_hat = cupy.vstack(
(population_hat, cupy.stack((a, b))))
else:
# append parents to the new population
if i == 0:
# if loop run for first time, then initialize the generation population
population_hat = cupy.stack((X, Y))
else:
# after first time, stack chromosomes to the generation population
population_hat = cupy.vstack(
(population_hat, cupy.stack((X, Y))))
return population_hat
def topk(array, k):
assert array.ndim == 2
top_i = array.astype('float32').argpartition(
-k
)[:, -k:] # cupy.argpartition do whole sorting for implementation reason
top_i = cp.flip(top_i, axis=1)
top_v = cp.vstack([array[r, top_i[r]] for r in range(array.shape[0])])
return top_v, top_i
def group_max(data, groups):
order = cp.lexsort(cp.vstack((data, groups)))
groups = groups[order] # this is only needed if groups is unsorted
data = data[order]
index = cp.empty(groups.shape[0], 'bool')
index[-1] = True
index[:-1] = groups[1:] != groups[:-1]
return data[index], index
def reverse1(d):
d = rev1.take(d).astype(np.uint16) # replace values in d by rev1[d]
d = d[d.min(1) != 0, :] - 1 # remove all batch-items that contain a 0
mask = d.max(1) > 0xFF
easy = d[~mask] # filter out trivial items that need no processing
hard = d[mask]
# del d # this clears out a lot of memory!
for i in range(32):
free()
mask = hard[:, i] > 0xFF
ignore = hard[~mask]
low = hard[mask]
high = hard[mask]
low[:, i] = low[:, i] & 0xFF
high[:, i] = high[:, i] >> 8
hard = np.vstack([ignore, low, high])
return np.vstack([easy, hard]).astype(np.uint8)
def update(self, dt):
phase_rep = cp.repeat(self.phase, self.size, axis=0)
phase_diff = cp.sum(self.coupling*cp.sin(phase_rep.T - phase_rep), axis=0)
dtheta = (self.internal_freq + phase_diff/self.size)
self.phase = cp.mod(self.phase + dtheta * dt, 2*np.pi)
self.hist = cp.vstack((self.hist, self.phase))
r, phi = self.order()
self.rs.append(r)
self.phis.append(phi)
def _Preprocess(T, H):
T = [t.data for t in T]
T = [cupy.vstack([cupy.zeros((self.fbsize, t.shape[1]), cupy.float32), t]) for t in T]
T = [cupy.hstack([t[i:len(t)-(self.fbsize-i)] for i in range(self.fbsize)]) for t in T]
T = [cupy.fft.irfft(cupy.exp(inum*cupy.angle(cupy.fft.rfft(t))))*dftnorm for t in T]
T = [Variable(t) for t in T]
H = [F.reshape(F.concat(F.broadcast_to(h, (self.fs, h.shape[0], h.shape[1])), axis=1), (h.shape[0]*self.fs, -1)) for h in H]
H = [F.concat([t, h]) for t, h in zip(T, H)]
return H
def generate_q_u_matrix(x_coordinate: cp.array,
y_coordinate: cp.array) -> tuple:
flatten_flag = x_coordinate.ndim > 1
if flatten_flag:
x_coordinate = x_coordinate.flatten()
y_coordinate = y_coordinate.flatten()
t, u = cp.modf(y_coordinate)
u = u.astype(int)
uy = cp.vstack([
cp.minimum(cp.maximum(u - 1, 0), h - 1),
cp.minimum(cp.maximum(u, 0), h - 1),
cp.minimum(cp.maximum(u + 1, 0), h - 1),
cp.minimum(cp.maximum(u + 2, 0), h - 1),
]).astype(int)
Qy = cp.dot(
coeff,
cp.vstack([
cp.ones_like(t, dtype=cp.float32), t,
cp.power(t, 2),
cp.power(t, 3)
]))
t, u = cp.modf(x_coordinate)
u = u.astype(int)
ux = cp.vstack([
cp.minimum(cp.maximum(u - 1, 0), w - 1),
cp.minimum(cp.maximum(u, 0), w - 1),
cp.minimum(cp.maximum(u + 1, 0), w - 1),
cp.minimum(cp.maximum(u + 2, 0), w - 1),
])
Qx = cp.dot(
coeff,
cp.vstack([
cp.ones_like(t, dtype=cp.float32), t,
cp.power(t, 2),
cp.power(t, 3)
]))
if flatten_flag:
Qx = Qx.reshape(4, frame_n, int(w * mag)).transpose(1, 0, 2).copy()
Qy = Qy.reshape(4, frame_n, int(h * mag)).transpose(1, 0, 2).copy()
ux = ux.reshape(4, frame_n, int(w * mag)).transpose(1, 0, 2).copy()
uy = uy.reshape(4, frame_n, int(h * mag)).transpose(1, 0, 2).copy()
return Qx, Qy, ux, uy
def update_mini_batch(self, mini_batch, eta):
"""Update the network's weights and biases by applying
gradient descent using backpropagation to a single mini batch.
The ``mini_batch`` is a list of tuples ``(x, y)``, and ``eta``
is the learning rate."""
xs, ys = mini_batch.T
xs = cp.array(
cp.vstack(xs).astype(np.float64).reshape((-1, self.sizes[0], 1)))
ys = cp.array(
cp.vstack(ys).astype(np.float64).reshape((-1, self.sizes[-1], 1)))
delta_nabla_b, delta_nabla_w = self.backprop_batch(xs, ys)
self.weights = [
w - eta * cp.mean(nw, axis=0)
for w, nw in zip(self.weights, delta_nabla_w)
]
self.biases = [
b - eta * cp.mean(nb, axis=0)
for b, nb in zip(self.biases, delta_nabla_b)
]
def fix_binary_predict_proba_result(proba):
if proba.ndim == 1:
if CumlToolBox.is_cupy_array(proba):
proba = cupy.vstack([1 - proba, proba]).T
else:
proba = cudf.Series(proba)
proba = cudf.concat([1 - proba, proba], axis=1)
elif proba.shape[1] == 1:
proba = cupy.hstack([1 - proba, proba])
return proba
def __init__(self,basis_number,extended_basis_number,t_start = 0,t_end=1,mempool=None):
self.basis_number = basis_number
self.extended_basis_number = extended_basis_number
self.basis_number_2D = (2*basis_number-1)*basis_number
self.basis_number_2D_ravel = (2*basis_number*basis_number-2*basis_number+1)
self.basis_number_2D_sym = (2*basis_number-1)*(2*basis_number-1)
self.extended_basis_number_2D = (2*extended_basis_number-1)*extended_basis_number
self.extended_basis_number_2D_sym = (2*extended_basis_number-1)*(2*extended_basis_number-1)
self.t_end = t_end
self.t_start = t_start
self.verbose = True
if mempool is None:
mempool = cp.get_default_memory_pool()
# pinned_mempool = cp.get_default_pinned_memory_pool()
# self.ix = cp.zeros((2*self.basis_number-1,2*self.basis_number-1),dtype=cp.int32)
# self.iy = cp.zeros((2*self.basis_number-1,2*self.basis_number-1),dtype=cp.int32)
temp = cp.arange(-(self.basis_number-1),self.basis_number,dtype=cp.int32)
# for i in range(2*self.basis_number-1):
# self.ix[i,:] = temp
# self.iy[:,i] = temp
self.ix,self.iy = cp.meshgrid(temp,temp)
if self.verbose:
print("Used bytes so far, after creating ix and iy {}".format(mempool.used_bytes()))
self.Ddiag = -(2*util.PI)**2*(self.ix.ravel(ORDER)**2+self.iy.ravel(ORDER)**2)
self.OnePerDdiag = 1/self.Ddiag
self.Dmatrix = cpx.scipy.sparse.diags(self.Ddiag,dtype=cp.float32)
if self.verbose:
print("Used bytes so far, after creating Dmatrix {}".format(mempool.used_bytes()))
# self.Imatrix = cp.eye((2*self.basis_number-1)**2,dtype=cp.int8)
self.Imatrix = cpx.scipy.sparse.identity((2*self.basis_number-1)**2,dtype=cp.float32)
self.Imatrix_dense = cp.eye((2*self.basis_number-1)**2,dtype=cp.float32)
if self.verbose:
print("Used bytes so far, after creating Imatrix {}".format(mempool.used_bytes()))
#K matrix --> 1D fourier index to 2D fourier index
ones_temp = cp.ones_like(temp)
self.Kmatrix = cp.vstack((cp.kron(temp,ones_temp),cp.kron(ones_temp,temp)))
if self.verbose:
print("Used bytes so far, after creating Kmatrix {}".format(mempool.used_bytes()))
#implement fft plan here
self._plan_fft2 = None
# self._fft2_axes = (-2, -1)
self._plan_ifft2 = None
x = np.concatenate((np.arange(self.basis_number)+1,np.zeros(self.basis_number-1)))
toep = sla.toeplitz(x)
self._Umask = cp.asarray(np.kron(toep,toep),dtype=cp.int16)
def main():
X, Y = getBinaryData()
X0 = X[Y == 0, :]
X1 = X[Y == 1, :]
X1 = cp.repeat(X1, 9, axis=0)
X = cp.vstack([X0, X1])
Y = cp.array([0] * len(X0) + [1] * len(X1))
model = LogisticModel()
model.fit(X, Y, show_fig=True)
model.score(X, Y)
def test_oob_coodinates():
offset = 2
idx = pyth_image.shape[0] + offset
prof = profile_line(pyth_image, (-offset, 2), (idx, 2),
linewidth=1,
order=0,
reduce_func=None,
mode='constant')
expected_prof = cp.vstack([
cp.zeros((offset, 1)), pyth_image[:, 2, cp.newaxis],
cp.zeros((offset + 1, 1))
])
assert_array_almost_equal(prof, expected_prof)
def block_hankel(data, f):
"""
Create a block hankel matrix.
Args:
data (float): Array.
f (int): number of rows
Returns:
Hankel matrix of f rows.
"""
n = data.shape[1] - f
return np.vstack(
[np.hstack([data[:, i + j] for i in range(f)]) for j in range(n)]).T
def predict(self,test_dl):
yps = []
self.model.load_state_dict(torch.load(self.params['model_path']))
for batch in test_dl:
xb, _ = batch
xb = xb.cuda()
pred = self.model(xb)
pred = to_dlpack(pred.detach())
yps.append(cp.fromDlpack(pred))
yps = cp.vstack(yps)
if yps.shape[1] == 1:
yps = yps.ravel()
return yps
def to_pixels(points, x_min, x_max, x_dim, y_min, y_max, y_dim):
pixels = np.vstack((points.real, points.imag)).T
pixels[:, 0] -= y_min
pixels[:, 1] -= x_min
pixels[:, 0] /= y_max - y_min
pixels[:, 1] /= x_max - x_min
pixels[:, 0] *= y_dim
pixels[:, 1] *= x_dim
pixels = pixels.astype(np.int32)
pixels = pixels[(pixels[:, 0] >= 0)
& (pixels[:, 0] < y_dim)
& (pixels[:, 1] >= 0)
& (pixels[:, 1] < x_dim)]
return pixels
def fit(self, include_be_used, is_test=False):
print('training started')
final_loss = 0.0
if is_test:
embeddings = self.model.embeddings_test = cp.vstack(
(self.model.embeddings_train, self.model.embeddings_test))
relations_matrix = self.model.relations_matrix_test
else:
embeddings = self.model.embeddings_train
relations_matrix = self.model.relations_matrix_train
self.losses = [0.0 for _ in range(self.model.epochs)]
start = time.time()
if include_be_used:
relations_matrix_transpose = relations_matrix.transpose().tocsr()
else:
relations_matrix_transpose = self.model.relations_matrix_test_not_be_used_from_other_class.transpose(
).tocsr()
self.model.loop_counter_for_negative_sampling = 0
for epoch in tqdm(range(self.model.epochs)):
learn_rate = self.calc_learn_rate(epoch)
target_embeddings = relations_matrix.dot(embeddings)
if epoch + 1 == self.model.epochs:
if is_test:
last_loss = self.calc_loss \
(embeddings[self.model.num_of_train_nodes:],
target_embeddings[self.model.num_of_train_nodes:])
else:
last_loss = self.calc_loss(embeddings, target_embeddings)
if math.isnan(last_loss):
return 999999
self.losses[epoch] = last_loss
final_loss = last_loss
# 各要素の想定ベクトルを計算してそれを用いて算出した傾きを元に勾配降下
self.calc_grad_and_update_embeddings(embeddings, target_embeddings,
is_test, learn_rate,
relations_matrix_transpose)
# negative sampling
# if do_negative_sampling:
self.negative_sampling(embeddings, is_test)
# norm罰則計算
self.norm_penalty(embeddings, is_test, learn_rate)
print('elapsed_time for training : {}'.format(time.time() - start))
return final_loss
def Generate_S1(J, T):
S = cp.random.normal(0, .01, J * 5)
S = S.reshape(5, 1, J)
EPS = cp.random.normal(0, .01, 3 * J * (T + 1))
EPS = EPS.reshape(3, T + 1, J)
Lx = cp.vstack(
(cp.array([0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),
cp.array([0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),
cp.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0])))
Li = np.vstack(
(cp.array([0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),
cp.array([0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]),
cp.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0])))
Lpi = cp.vstack(
(cp.array([0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),
cp.array([0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),
cp.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0])))
R = cp.reshape(
np.vstack(
(cp.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]),
cp.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0]),
cp.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0]))), [3, 18])
U = cp.reshape(
np.vstack(
(cp.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0]),
cp.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0]),
cp.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1]))), [3, 18])
I = cp.reshape(
cp.vstack(
(cp.array([1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),
cp.array([0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),
cp.array([0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0]))), [3, 18])
I = cp.repeat(I[:, :, cp.newaxis], J, axis=2)
return EPS, S, Lx, Lpi, Li, I, R, U
def _project_slab(self, axis):
proj = []
for data in self._load_slab():
func = getattr(data, self._mode)
proj.append(func(axis=axis))
# release the reference in this loop
func = None
data = None
if axis == 0:
# z is an aggregated axis, sum together
proj = sum(proj)
else:
# z is a visible axis, stack together
proj = cp.vstack(proj)
return cp.asnumpy(proj)
def _shift_selem(selem, shift_x, shift_y):
"""Shift the binary image `selem` in the left and/or up.
This only affects 2D structuring elements with even number of rows
or columns.
Parameters
----------
selem : 2D array, shape (M, N)
The input structuring element.
shift_x, shift_y : bool
Whether to move `selem` along each axis.
Returns
-------
out : 2D array, shape (M + int(shift_x), N + int(shift_y))
The shifted structuring element.
"""
if selem.ndim != 2:
# do nothing for 1D or 3D or higher structuring elements
return selem
m, n = selem.shape
if m % 2 == 0:
extra_row = cp.zeros((1, n), selem.dtype)
if shift_x:
selem = cp.vstack((selem, extra_row))
else:
selem = cp.vstack((extra_row, selem))
m += 1
if n % 2 == 0:
extra_col = cp.zeros((m, 1), selem.dtype)
if shift_y:
selem = cp.hstack((selem, extra_col))
else:
selem = cp.hstack((extra_col, selem))
return selem
def one_step_for_sqrtBetas(self, Layers):
sqrt_beta_noises = self.stdev_sqrtBetas * cp.random.randn(
self.n_layers)
propSqrtBetas = cp.zeros(self.n_layers, dtype=cp.float32)
for i in range(self.n_layers):
temp = cp.sqrt(1 - self.pcn_step_sqrtBetas**2) * Layers[
i].sqrt_beta + self.pcn_step_sqrtBetas * sqrt_beta_noises[i]
propSqrtBetas[i] = max(temp, 1e-4)
if i == 0:
stdev_sym_temp = (propSqrtBetas[i] /
Layers[i].sqrt_beta) * Layers[i].stdev_sym
Layers[i].new_sample_sym = stdev_sym_temp * Layers[
i].current_noise_sample
else:
Layers[i].LMat.construct_from_with_sqrt_beta(
Layers[i - 1].new_sample, propSqrtBetas[i])
if i < self.n_layers - 1:
Layers[i].new_sample_sym = cp.linalg.solve(
Layers[i].LMat.latest_computed_L,
Layers[i].current_noise_sample)
else:
wNew = Layers[-1].current_noise_sample
eNew = cp.random.randn(self.measurement.num_sample)
wBar = cp.concatenate((eNew, wNew))
LBar = cp.vstack(
(self.H, Layers[-1].LMat.latest_computed_L))
v, res, rnk, s = cp.linalg.lstsq(LBar, self.yBar - wBar)
Layers[-1].new_sample_sym = v
Layers[i].new_sample = Layers[i].new_sample_sym[
self.fourier.basis_number_2D_ravel - 1:]
logRatio = 0.5 * (util.norm2(self.y / self.measurement.stdev -
self.H @ Layers[-1].current_sample_sym))
logRatio -= 0.5 * (util.norm2(self.y / self.measurement.stdev -
self.H @ Layers[-1].new_sample_sym))
if logRatio > cp.log(cp.random.rand()):
# print('Proposal sqrt_beta accepted!')
self.Layers_sqrtBetas = propSqrtBetas
for i in range(self.n_layers):
Layers[i].sqrt_beta = propSqrtBetas[i]
Layers[i].LMat.set_current_L_to_latest()
if Layers[i].is_stationary:
Layers[i].stdev_sym = stdev_sym_temp
Layers[i].stdev = Layers[i].stdev_sym[
self.fourier.basis_number_2D_ravel - 1:]
def calculate_W_out(self, Y_target, N_x, beta, train_start_timestep,
train_end_timestep):
# see Lukosevicius Practical ESN eqtn 11
# Using ridge regression
N_u = sp.shape(Y_target)[0]
X = cp.array(
cp.vstack(
(cp.ones((1, train_end_timestep - train_start_timestep)),
cp.array(Y_target[:,
train_start_timestep:train_end_timestep]),
self.x[train_start_timestep:train_end_timestep].transpose())))
# Ridge Regression
self.W_out = cp.asnumpy(cp.matmul(cp.array(Y_target[:, train_start_timestep+1:train_end_timestep+1]),\
cp.matmul(X.transpose(),
cp.array(cp.linalg.inv(
cp.add(cp.matmul(X,X.transpose()),beta*cp.identity(1+N_x+N_u)))))))
def test_vstack_wrong_ndim(self):
a = cupy.empty((3,))
b = cupy.empty((3, 1))
with self.assertRaises(ValueError):
cupy.vstack((a, b))
t = chainer.Variable(cp.asarray(train_y[perm[i:i + BATCH_SIZE]]), volatile='off')
optimizer.update(model, x, t)
train_loss += float(model.loss.data) * len(t.data)
train_accuracy += float(model.accuracy.data) * len(t.data)
epoch_result = None
test_accuracy, test_loss = 0, 0
for i in range(0, NUM_TEST, BATCH_SIZE):
x = chainer.Variable(cp.asarray(test_x[i:i + BATCH_SIZE]), volatile='on')
t = chainer.Variable(cp.asarray(test_y[i:i + BATCH_SIZE]), volatile='on')
batch_result = model(x, t, False)
if epoch == NUM_EPOCH:
if i == 0:
epoch_result = batch_result
else:
epoch_result.data = cp.vstack((epoch_result.data, batch_result.data))
test_loss += float(model.loss.data) * len(t.data)
test_accuracy += float(model.accuracy.data) * len(t.data)
print 'epoch-{}-{}: [TRAIN] loss: {} accuracy: {}, [TEST] loss: {} accuracy: {}'.format(j+1, epoch,
train_loss / NUM_TRAIN, train_accuracy / NUM_TRAIN, test_loss / NUM_TEST, test_accuracy / NUM_TEST)
if epoch == NUM_EPOCH:
if j == 0:
all_results = epoch_result
else:
all_results += epoch_result
y = chainer.Variable(cp.asarray(test_y), volatile='on')
en_acc = F.accuracy(all_results, y)
print 'ENSUMBLE RESULT:\n\t accuracy: {}'.format(en_acc.data)
