def ssb_kernel(processed4D,real_calibration,aperture,voltage):
data_size = processed4D.shape
wavelength = e_lambda(voltage)
cutoff = aperture/wavelength
four_y = np.fft.fftshift(np.fft.fftfreq(data_size[0], real_calibration))
four_x = np.fft.fftshift(np.fft.fftfreq(data_size[1], real_calibration))
Four_Y,Four_X = np.meshgrid(four_y,four_x)
FourXY = np.sqrt((Four_Y ** 2) + (Four_X**2))
Left_Lobe = np.zeros(data_size,dtype=bool)
RightLobe = np.zeros_like(Left_Lobe)
#convert to CuPy arrays
Four_Y = cp.asarray(Four_Y)
Four_X = cp.asarray(Four_X)
FourXY = cp.asarray(FourXY)
Left_Lobe = cp.asarray(Left_Lobe)
RightLobe = cp.asarray(RightLobe)
rsize = cp.asarray((data_size[0],data_size[1]),dtype=int)
#pass to JIT kernel
lobe_calc(Left_Lobe,RightLobe,Four_Y,Four_X,FourXY,rsize,cutoff)
data_phase = phase_cupy(processed4D)
data_ampli = ampli_cupy(processed4D)
left_trotter = cp.multiply(data_ampli[Left_Lobe],cp.exp((1j)*data_phase[Left_Lobe]))
left_image = cp.asnumpy(cp.fft.ifft2(cp.sum(left_trotter,axis=-1)))
righttrotter = cp.multiply(data_ampli[RightLobe],cp.exp((1j)*data_phase[RightLobe]))
rightimage = cp.asnumpy(cp.fft.ifft2(cp.sum(righttrotter,axis=-1)))
return left_image,right_image
def inverse_transform(self, array_in, array_out):
"""
Perform the inverse Fourier transform of array_in,
and store the result in array_out
Parameters
----------
array_in, array_out: cuda device arrays or numpy arrays
When using the GPU, these should be cuda device array.
When using the CPU, array_in should be one of the
two buffers that are returned by `get_buffers`
"""
if self.use_cuda:
# Copy 2D arrays to 1D array for optimized 1D batch FFT
cuda_copy_2d_to_1d[self.dim_grid, self.dim_block](array_in,
self.buffer1d_in)
# Perform forward FFT
self.fft.fft(self.buffer1d_in, self.buffer1d_out,
cufft.CUFFT_INVERSE)
# Normalize inverse FFT
cupy.multiply(self.buffer1d_out,
self.inv_Nz,
out=self.buffer1d_out)
# Copy 1D arrays back to 2D array
cuda_copy_1d_to_2d[self.dim_grid,
self.dim_block](self.buffer1d_out, array_out)
elif self.use_mkl:
# Perform the inverse FFT on the CPU using MKL
self.mklfft.inverse_transform(array_in, array_out)
else:
# Perform the inverse FFT on the CPU using FFTW
self.ifft.update_arrays(new_input_array=array_in,
new_output_array=array_out)
self.ifft()
def back_prop(self, dloss_dy):
"""
Do backward propagation through the layer.
Parameters
----------
dloss_dy : cp.array of floats, shape (number of examples,) + self.output_size
Derivative of loss with respect to output values.
Returns
-------
cp.array of floats, shape (number of examples,) + self.input_size
Derivative of loss with respect to input values.
"""
# Keep track of all the dimensions.
nr_examples = dloss_dy.shape[0]
a, b, _ = self.input_size
m, n, nr_channels = self.output_size
p, q = self.pool_size
# Expand the derivative to the input shape.
dloss_dy_reshaped = dloss_dy.reshape(
(nr_examples, m, 1, n, 1, nr_channels))
dloss_dy_expanded = cp.multiply(
dloss_dy_reshaped, cp.ones((1, 1, p, 1, q, 1), dtype=cp.int8))
dloss_dy_expanded = dloss_dy_expanded.reshape(
(nr_examples, a, b, nr_channels))
# Apply the cached mask to the derivative.
return cp.multiply(dloss_dy_expanded, self.i_cache)
def _derivativenorm(self):
"""Compute the derivative of the norm
Returns
-------
derivative : numpy array, shape (m_parameters,)
"""
w2 = cp.reshape(self.w,(self.n_features,self.d,self.D,self.D))
derivative = cp.zeros((self.n_features,self.d,self.D,self.D))
tmp=cp.zeros((self.n_features,self.D))
tmp2=cp.zeros((self.n_features,self.D))
tmp[0,:]=cp.sum(cp.square(w2[0,:,0,:]),0)
for i in range(1,self.n_features-1):
tmp[i,:]=cp.dot(tmp[i-1,:],cp.sum(cp.square(w2[i,:,:,:]),0))
tmp[self.n_features-1,:]=cp.inner(tmp[self.n_features-2,:],
cp.sum(cp.square(w2[self.n_features-1,:,:,0]),0))
tmp2[self.n_features-1,:]=cp.sum(cp.square(w2[self.n_features-1,:,:,0]),0)
for i in range(self.n_features-2,-1,-1):
tmp2[i,:]=cp.dot(cp.sum(cp.square(w2[i,:,:,:]),0),tmp2[i+1,:])
tmp2[0,:]=cp.inner(cp.sum(cp.square(w2[0,:,0,:]),0),tmp2[1,:])
for j in range(self.d):
derivative[0,j,0,:]=cp.multiply(tmp2[1,:],2*(w2[0,j,0,:]))
derivative[self.n_features-1,j,:,0]=\
cp.multiply(tmp[self.n_features-2,:],2*(w2[self.n_features-1,j,:,0]))
for i in range(1,self.n_features-1):
temp3=cp.outer(tmp[i-1,:],tmp2[i+1,:])
for j in range(self.d):
derivative[i,j,:,:]=cp.multiply(temp3,2*(w2[i,j,:,:]))
return derivative.reshape(self.m_parameters)
def get_flat_dpc(data4D_flat, chunks=8, centered=True):
stops = np.zeros(chunks + 1, dtype=np.int)
stops[0:chunks] = np.arange(0, data4D_flat.shape[0],
(data4D_flat.shape[0] / chunks))
stops[chunks] = data4D_flat.shape[0]
if centered:
cent_x = cp.asarray(data4D_flat.shape[2]) / 2
cent_y = cp.asarray(data4D_flat.shape[1]) / 2
else:
CentralDisk = np.median(data4D_flat, axis=0)
cent_x, cent_y, _ = st.util.sobel_circle(CentralDisk)
cent_x = cp.asarray(cent_x)
cent_y = cp.asarray(cent_y)
yy, xx = cp.mgrid[0:data4D_flat.shape[1], 0:data4D_flat.shape[2]]
FlatSum = cp.asarray(np.sum(data4D_flat, axis=(-1, -2)))
YCom_CPU = np.zeros(data4D_flat.shape[0], dtype=data4D_flat.dtype)
XCom_CPU = np.zeros(data4D_flat.shape[0], dtype=data4D_flat.dtype)
for ii in range(chunks):
startval = stops[ii]
stop_val = stops[ii + 1]
gpu_4Dchunk = cp.asarray(data4D_flat[startval:stop_val, :, :])
FlatY = cp.multiply(gpu_4Dchunk, yy)
FlatX = cp.multiply(gpu_4Dchunk, xx)
YCom = (cp.sum(FlatY, axis=(-1, -2)) /
FlatSum[startval:stop_val]) - cent_y
XCom = (cp.sum(FlatX, axis=(-1, -2)) /
FlatSum[startval:stop_val]) - cent_x
YCom_CPU[startval:stop_val] = cp.asnumpy(YCom)
XCom_CPU[startval:stop_val] = cp.asnumpy(XCom)
del YCom, XCom, gpu_4Dchunk, cent_x, cent_y, FlatSum
return YCom_CPU, XCom_CPU
def backward_pass(x, y, output, hidden_output, W_output):
output_error = -(y - output) # Calculate error
output_over_net = output*(1 - output) # Derivative of sigmoid function
sigmoid_on_error = cp.multiply(output_error, output_over_net) # Calculate the sigmoid function's affect on error
W_output = cp.transpose(W_output)
hidden_error = cp.dot(sigmoid_on_error, W_output) # Calculate the affect of output weights on hidden weights' error
hidden_over_net = hidden_output*(1 - hidden_output) # Derivative of sigmoid function
sigmoid_on_hidden_error = cp.multiply(hidden_error, hidden_over_net) # Calculate the sigmoid function's affect on error
# Correctly arrange matrices for calculations
x = cp.atleast_2d(x)
hidden_output = cp.atleast_2d(hidden_output)
x_transpose = cp.transpose(x)
hidden_output_transpose = cp.transpose(hidden_output)
sigmoid_on_hidden_error = sigmoid_on_hidden_error.reshape(1, sigmoid_on_hidden_error.size)
sigmoid_on_error = sigmoid_on_error.reshape(1, sigmoid_on_error.size)
# Calculate weight changes
W_hidden_c = cp.dot(x_transpose, sigmoid_on_hidden_error)
W_output_c = cp.dot(hidden_output_transpose, sigmoid_on_error)
# Calculate bias changes
B_hidden_c = sigmoid_on_hidden_error
B_output_c = sigmoid_on_error
return W_output_c, W_hidden_c, B_hidden_c, B_output_c
def normal(loc=0.0, scale=1.0, size=None, dtype=float):
"""Returns an array of normally distributed samples.
Args:
loc (float or array_like of floats): Mean of the normal distribution.
scale (float or array_like of floats):
Standard deviation of the normal distribution.
size (int or tuple of ints): The shape of the array. If ``None``, a
zero-dimensional array is generated.
dtype: Data type specifier. Only :class:`numpy.float32` and
:class:`numpy.float64` types are allowed.
Returns:
cupy.ndarray: Normally distributed samples.
.. seealso:: :func:`numpy.random.normal`
"""
rs = _generator.get_random_state()
if size is None and any(isinstance(arg, cupy.ndarray)
for arg in [scale, loc]):
size = cupy.broadcast_arrays(loc, scale)[0].shape
x = rs.normal(0, 1, size, dtype)
cupy.multiply(x, scale, out=x)
cupy.add(x, loc, out=x)
return x
def normal(self, loc=0.0, scale=1.0, size=None, dtype=float):
"""Returns an array of normally distributed samples.
.. seealso::
- :func:`cupy.random.normal` for full documentation
- :meth:`numpy.random.RandomState.normal`
"""
dtype = _check_and_get_dtype(dtype)
if size is None:
size = cupy.broadcast(loc, scale).shape
if dtype.char == 'f':
func = curand.generateNormal
else:
func = curand.generateNormalDouble
if isinstance(scale, cupy.ndarray):
x = self._generate_normal(func, size, dtype, 0.0, 1.0)
cupy.multiply(x, scale, out=x)
cupy.add(x, loc, out=x)
elif isinstance(loc, cupy.ndarray):
x = self._generate_normal(func, size, dtype, 0.0, scale)
cupy.add(x, loc, out=x)
else:
x = self._generate_normal(func, size, dtype, loc, scale)
return x
def calculate_loss_modified(prediction, y):
prediction[prediction == 0] = 0.00000001
y[y == 0] = 0.00000001
lossExpression = -cp.sum(
cp.multiply(y, cp.log(prediction)) + cp.multiply(
cp.ones(y.shape) - y, cp.log(cp.ones(y.shape) - prediction)))
return lossExpression
def __call__(self, params, g_params):
new_params = tuple(
cp.add(
param,
cp.subtract(cp.multiply(self.momentum, cp.subtract(param, v)),
cp.multiply(self.rate, g_param)))
for param, g_param, v in zip(params, g_params, self.v))
self.v = params
return new_params
def __iteration(self, gamma: cp.float64):
"""
Iteration of the inner loop of the (iterated) Tikhonov method.
:param gamma: Regularization parameter of the Tikhonov algorithm.
:type gamma: float
:return: Numpy array with the solution in given iteration.
"""
LU, P = linalg.lu_factor(cp.add(self.KHK, cp.multiply(gamma, self.identity)))
self.current = linalg.lu_solve((LU, P), cp.add(self.q_estimator, cp.multiply(gamma, self.previous)))
def gpu_gaussian(self, a, b, s):
km = cp.empty(shape=(a.shape[0], b.shape[0]), dtype=a.dtype)
km = cp.multiply(cp.dot(a, b.T, out=km), -2, out=km)
km += cp.power(a, 2).sum(axis=1).reshape(-1, 1)
km += cp.power(b, 2).sum(axis=1)
cp.multiply(km, -1 / (2 * s * s), out=km)
cp.exp(km, out=km)
return km
def pulse_compression(x, template, normalize=False, window=None, nfft=None):
"""
Pulse Compression is used to increase the range resolution and SNR
by performing matched filtering of the transmitted pulse (template)
with the received signal (x)
Parameters
----------
x : ndarray
Received signal, assume 2D array with [num_pulses, sample_per_pulse]
template : ndarray
Transmitted signal, assume 1D array
normalize : bool
Normalize transmitted signal
window : array_like, callable, string, float, or tuple, optional
Specifies the window applied to the signal in the Fourier
domain.
nfft : int, size of FFT for pulse compression. Default is number of
samples per pulse
Returns
-------
compressedIQ : ndarray
Pulse compressed output
"""
[num_pulses, samples_per_pulse] = x.shape
if nfft is None:
nfft = samples_per_pulse
if window is not None:
Nx = len(template)
if callable(window):
W = window(cp.fft.fftfreq(Nx))
elif isinstance(window, cp.ndarray):
if window.shape != (Nx, ):
raise ValueError("window must have the same length as data")
W = window
else:
W = get_window(window, Nx, False)
template = cp.multiply(template, W)
if normalize is True:
template = cp.divide(template, cp.linalg.norm(template))
fft_x = cp.fft.fft(x, nfft)
fft_template = cp.conj(cp.tile(cp.fft.fft(template, nfft),
(num_pulses, 1)))
compressedIQ = cp.fft.ifft(cp.multiply(fft_x, fft_template), nfft)
return compressedIQ
def feedforward(self, input):
self.input = input
self.ft = sigmoid(self.Wf@input + [email protected]_ht + self.bf) # forget gate
self.it = sigmoid(self.Wi@input + [email protected]_ht + self.bi) # update gate
self.ot = sigmoid(self.Wo@input + [email protected]_ht + self.bo) # output gate
self.ct_bar = tanh(self.Wc @ input + self.Uc @ self.prev_ht + self.bc)
# outputs
self.ct = cp.multiply(self.ft, self.prev_ct) + cp.multiply(self.it, self.ct_bar)
self.ht = cp.multiply(self.ot, tanh(self.ct))
return self.ct, self.ht
def update_reservoir(self, u, n, Y):
# u is input at specific time
# u has shape (N_u (3 for L63))
# See page 16 eqtn 18 of Lukosevicius PracticalESN for feedback info.
x_n_tilde = cp.tanh(
cp.matmul(self.W, self.x[n]) +
cp.array(sp.matmul(self.W_in, sp.hstack((sp.array([1]), u)))) +
cp.array(sp.matmul(self.W_fb, Y)))
self.x[n+1] = cp.multiply((1-cp.array(self.alpha_matrix)), cp.array(self.x[n])) \
+ cp.multiply(cp.array(self.alpha_matrix), x_n_tilde)
def diffuse_slime_trail():
cp.multiply(
SlimeWorld.cells,
SlimeWorld.trail_reduction_factor,
out=SlimeWorld.cells,
casting="unsafe",
)
convolve(SlimeWorld.cells,
SlimeWorld.trail_kernel,
output=SlimeWorld.cells)
def error_minimization(W,
b,
zeta,
a,
prev_layer,
activation_func,
den_activation,
y,
w=None,
d=None,
y_pred=None):
dW = {}
dB = {}
delta = {}
try:
batch_size = y.shape[1]
except IndexError:
batch_size = 1
y = cp.reshape(y, (y.shape[0], batch_size))
is_last_layer = (type(w) == type(d)) and (type(d) == type(None))
if is_last_layer:
delta['s'] = cp.subtract(a['s'], y)
dB['s'] = (1 / batch_size) * cp.sum(delta['s'], axis=1)
dB['s'] = cp.reshape(dB['s'], (dB['s'].shape[0], 1, 1))
delta['s'] = cp.reshape(delta['s'],
(delta['s'].shape[0], 1, delta['s'].shape[1]))
dW['s'] = (1 / batch_size) * cp.einsum('nik,kjn->nij', delta['s'],
a['d'].T)
else:
w = cp.array(w)
deltaW = cp.einsum('nik,kij->nj', w.T, d)
deltaW = cp.reshape(deltaW, (deltaW.shape[0], 1, deltaW.shape[1]))
a_der = activation(str(activation_func) + '_der', zeta['s'])
delta['s'] = cp.multiply(deltaW, a_der)
dB['s'] = (1 / batch_size) * cp.sum(delta['s'].squeeze(), axis=1)
dB['s'] = cp.reshape(dB['s'], (dB['s'].shape[0], 1, 1))
dW['s'] = (1 / batch_size) * cp.einsum('nik,kjn->nij', delta['s'],
a['d'].T)
deltaW = cp.einsum('nik,kij->knj', W['s'].T, delta['s'])
a_der = activation(den_activation + '_der', zeta['d'])
delta['d'] = cp.multiply(deltaW, a_der)
dB['d'] = (1 / batch_size) * cp.sum(delta['d'], axis=2)
dB['d'] = cp.reshape(dB['d'], (dB['d'].shape[0], dB['d'].shape[1], 1))
dW['d'] = (1 / batch_size) * cp.dot(delta['d'], prev_layer.T)
return [dW, dB, delta]
def func(a, t, params, A, function, bT, x, division):
index = int(t * (division - 1))
return cp.multiply(
-1.,
cp.add(
cp.dot(a, params[1][index]),
cp.dot(
cp.multiply(
bT,
cp.multiply(params[0][index],
function(cp.dot(x[index], A.T)))), A)))
def dyad_transform(self, grids):
"""
Experimental: compute spectrum of the dyad v_i * v_j
"""
k_xx = grids.fourier_transform(function=cp.multiply(self.arr[0, 1:-1, :, 1:-1, :],
self.arr[0, 1:-1, :, 1:-1, :]))
k_xy = grids.fourier_transform(function=cp.multiply(self.arr[0, 1:-1, :, 1:-1, :],
self.arr[1, 1:-1, :, 1:-1, :]))
k_yy = grids.fourier_transform(function=cp.multiply(self.arr[1, 1:-1, :, 1:-1, :],
self.arr[1, 1:-1, :, 1:-1, :]))
self.dyad_spectrum = cp.array([[k_xx, k_xy], [k_xy, k_yy]])
def calcDistField(point_file, h5name, save_location):
data_file = h5py.File(h5name)
data = data_file['data'][:]
data_dim = data.shape[0]
data_file.close()
ptfile = h5py.File(point_file)
sample_points = ptfile['points'][:]
ptfile.close()
sample_size = sample_points.shape[0]
#gpu parallelization
memory_pool = cupy.get_default_memory_pool()
pinned_memory_pool = cupy.get_default_pinned_memory_pool()
distancesgpu = numpy.zeros((data_dim, data.shape[1], sample_size))
x = cupy.asarray(sample_points)
allpts = cupy.tile(x, (data.shape[1], 1))
blocks = int(numpy.ceil(sample_size * data.shape[1] / 8192))
del x
print(blocks)
yy = cupy.asarray(data)
for inst in range(data_dim):
if inst % 200 == 0:
print(inst)
y = yy[inst]
xx = allpts + cupy.tile(y, (1, sample_size)).reshape(-1, 3)
xdot = cupy.sum(cupy.multiply(xx, xx), axis=1)
dt = cupy.zeros((sample_size * data.shape[1], ))
for blk in range(blocks):
idstart = int(blk * 8192)
idend = int((blk + 1) * 8192)
dists = cupy.tile(xdot[idstart:idend], (y.shape[0], 1)).transpose(
) - 2 * cupy.matmul(xx[idstart:idend], y.transpose()) + cupy.tile(
cupy.sum(cupy.multiply(y, y), axis=1).transpose(),
(xx[idstart:idend].shape[0], 1))
dt[idstart:idend] = cupy.amin(dists, axis=1)
del dists
dt = cupy.reshape(dt, (-1, sample_size))
distancesgpu[inst] = cupy.asnumpy(dt)
del dt
del xx
del xdot
memory_pool.free_all_blocks()
pinned_memory_pool.free_all_blocks()
# save file
saveh5 = h5py.File(save_location, 'w')
saveh5.create_dataset('distances', data=distancesgpu)
saveh5.close()
def _preprocess(self, frame):
frame_dev = cp.asarray(frame)
# resize
zoom = np.roll(self.inp_handle.shape, -1) / frame_dev.shape
small_dev = cupyx.scipy.ndimage.zoom(frame_dev,
zoom,
order=1,
mode='opencv',
grid_mode=True)
# BGR to RGB
rgb_dev = small_dev[..., ::-1]
# HWC -> CHW
chw_dev = rgb_dev.transpose(2, 0, 1)
# normalize to [0, 1] interval
cp.multiply(chw_dev, 1 / 255., out=self.inp_handle)
def do_rmsprop(self, X, Y, update, learning_rate, **kwargs):
layers = len(self.structure) - 1
grads = self.calculate_grads(X, Y, kwargs["l2_reg_param"])
for ii in cp.arange(1, layers + 1):
update["w" + str(ii)] = kwargs["beta"] * update.get(
"w" + str(ii), 0) + (1 - kwargs["beta"]) * cp.square(
cp.sum(grads["w" + str(ii)], axis=0))
update["b" + str(ii)] = kwargs["beta"] * update.get(
"b" + str(ii), 0) + (1 - kwargs["beta"]) * cp.square(
cp.sum(grads["b" + str(ii)], axis=1).reshape(-1, 1))
self.params["w"+str(ii)] -= cp.multiply((learning_rate/ cp.sqrt(kwargs["epsilon"] + update["w"+str(ii)])),\
cp.sum(grads["w"+str(ii)],axis=0))
self.params["b"+str(ii)] -= cp.multiply((learning_rate / cp.sqrt(kwargs["epsilon"] + update["b"+str(ii)])),\
cp.sum(grads["b"+str(ii)],axis=1).reshape(-1,1))
return update
def calcMSeries(t_axis, h, t_0, m_0, noiseFlag = False):
if noiseFlag:
# RK4 solution with thermal noise
m_rk4 = []
m_prev = m_0
t_prev = t_0
H_eff = cp.array([0, 0, float(H_k*m_0[2])]) + cp.multiply(demag_const, m_0) + cp.array([thermalConst*cp.random.normal(0,1),
thermalConst*cp.random.normal(0,1),
thermalConst*cp.random.normal(0,1)])
for t in t_axis:
print("#################################################################")
print("step t:"+str(t))
new_m = llgsRK4Heun(t_prev, m_prev, t, h, H_eff)
print("new m:"+str(new_m))
m_rk4.append(new_m)
m_prev = new_m
t_prev = t
H_eff = cp.array([0, 0, float(H_k*new_m[2])]) + cp.multiply(demag_const, new_m) + cp.array([thermalConst*cp.random.normal(0,1),
thermalConst*cp.random.normal(0,1),
thermalConst*cp.random.normal(0,1)])
print("new H_eff:"+str(H_eff))
else:
# RK4 solution without thermal noise
m_rk4 = []
m_prev = m_0
t_prev = t_0
H_eff = cp.array([0, 0, float(H_k*m_0[2])]) + cp.multiply(demag_const, m_0)
for t in t_axis:
print("#################################################################")
print("step t:"+str(t))
new_m = llgsRK(t_prev, m_prev, t, h, H_eff)
print("new m:"+str(new_m))
m_rk4.append(new_m)
m_prev = new_m
t_prev = t
H_eff = cp.array([0, 0, float(H_k*new_m[2])]) + cp.multiply(demag_const, new_m)
print("new H_eff:"+str(H_eff))
# change all elements to list and float
for i in range(0, len(m_rk4)):
m_rk4[i] = m_rk4[i].tolist()
for j in range(0, len(m_rk4[i])):
m_rk4[i][j] = float(m_rk4[i][j])
return m_rk4
def var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
"""Returns the variance along an axis.
Args:
a (cupy.ndarray): Array to compute variance.
axis (int): Along which axis to compute variance. The flattened array
is used by default.
dtype: Data type specifier.
out (cupy.ndarray): Output array.
keepdims (bool): If True, the axis is remained as an axis of size one.
Returns:
cupy.ndarray: The variance of the input array along the axis.
.. seealso:: :func:`numpy.var`
"""
if axis is None:
axis = tuple(range(a.ndim))
if not isinstance(axis, tuple):
axis = (axis,)
if dtype is None and issubclass(a.dtype.type,
(numpy.integer, numpy.bool_)):
dtype = numpy.dtype(numpy.float64)
arrmean = mean(a, axis=axis, dtype=dtype, keepdims=True)
x = cupy.subtract(a, arrmean, dtype=dtype)
cupy.square(x, x)
ret = cupy.sum(x, axis=axis, dtype=dtype, out=out, keepdims=keepdims)
rcount = max(_count_reduce_items(a, axis) - ddof, 0)
return cupy.multiply(ret, ret.dtype.type(1.0 / rcount), out=ret)
def GlobalReg(X, T, sigma2, outliers):
"""
:params:
:return
"""
[N, D] = X.shape
M = T.shape[0]
# Calculate P matrix
# Nominator of P
P_num = cp.sum((X[None, :, :] - T[:, None, :])**2, axis=2)
P_num = cp.exp(-P_num / (2 * sigma2))
# Denominator of P
P_den = cp.sum(P_num, axis=0)
P_den = cp.tile(P_den, (M, 1))
P_den[P_den == 0] = 2.220446049250313e-16
c = ((((2 * cp.pi * sigma2)**D / 2) * (outliers / (1 - outliers))) *
(M / N))
P_den += c
P = cp.divide(P_num, P_den)
P1 = cp.sum(P, axis=1)
Pt1 = cp.sum(P, axis=0)
c1 = c * cp.ones(N)
K1 = cp.dot(cp.transpose(P_num), cp.ones(M))
a = cp.tile(cp.divide(1, K1 + c1).reshape(N, 1), D)
Px = cp.dot(P_num, (cp.multiply(a, X)))
return P1, Pt1, Px
def step(self, model, solution, old_solution, *args, **kwargs):
"""Correlation coefficient at current step.
Args:
model (tomomak.Model): used model.
solution (ndarray): supposed solution.
old_solution (ndarray): supposed_solution at a previous iteration.
*args: not used, but needed to be here in order to work with Solver properly.
**kwargs: not used, but needed to be here in order to work with Solver properly.
Returns:
float: correlation coefficient.
"""
det_num = model.detector_signal.shape[0]
det_num2 = det_num**2
f_s = cp.sum(old_solution)
f_new_s = cp.sum(solution)
corr = det_num2 * cp.sum(np.multiply(solution, old_solution))
corr = corr - f_s * f_new_s
divider = det_num2 * cp.sum(np.multiply(solution, solution))
tmp = f_new_s**2
divider = cp.sqrt(divider - tmp)
corr = corr / divider
divider = det_num2 * cp.sum(cp.multiply(old_solution, old_solution))
tmp = f_s**2
divider = cp.sqrt(divider - tmp)
if divider:
res = corr / divider
else:
res = np.nan
self.data.append(res)
self.data.append(res)
return res
def kron(a, b):
"""Returns the kronecker product of two arrays.
Args:
a (~cupy.ndarray): The first argument.
b (~cupy.ndarray): The second argument.
Returns:
~cupy.ndarray: Output array.
.. seealso:: :func:`numpy.kron`
"""
a_ndim = a.ndim
b_ndim = b.ndim
if a_ndim == 0 or b_ndim == 0:
return cupy.multiply(a, b)
ndim = b_ndim
a_shape = a.shape
b_shape = b.shape
if a_ndim != b_ndim:
if b_ndim > a_ndim:
a_shape = (1,) * (b_ndim - a_ndim) + a_shape
else:
b_shape = (1,) * (a_ndim - b_ndim) + b_shape
ndim = a_ndim
axis = ndim - 1
out = core.tensordot_core(a, b, None, a.size, b.size, 1, a_shape + b_shape)
for _ in six.moves.range(ndim):
out = core.concatenate_method(out, axis=axis)
return out
def backward(self, dA):
"""Implementation of backward pooling using stride tricks.
Args:
dA (np.array): derivative of output values
Returns:
np.array: derivative of intput values
"""
if len(dA.shape) == 2:
dA = dA.reshape(dA.shape[1], *self.dim_out[1:])
self.dX[:, :, :, :] = 0
n_h = self.dim_out[1]
n_w = self.dim_out[2]
shape = (
self.X.shape[0], # m
n_h,
n_w,
self.f,
self.f,
self.X.shape[-1]) # n_c
strides = (self.X.strides[0], self.X.strides[1] * self.stride,
self.X.strides[2] * self.stride, self.X.strides[1],
self.X.strides[2], self.X.strides[3])
M = np.lib.stride_tricks.as_strided(
self.X, shape=shape, strides=strides) # , writeable=False)
# dangerous: writing into memory, don't mess up strides !
M_dX = np.lib.stride_tricks.as_strided(
self.dX, shape=shape, strides=strides) # , writeable=True)
mask = np.max(M, axis=(-3, -2), keepdims=True) == M
M_dX += np.multiply(mask, dA[:, :, :, None, None])
return self.dX
def fit_dropout(self, epochs=1, batch_size=1, p=0.5, gamma=0.9, **args):
X = args['X_train']
y = args['y_train']
if 'verbose' in args:
verbose = args['verbose']
else:
verbose = None
loss_val = cp.zeros((cp.int(epochs)))
par_gpu = deepcopy(self.start)
momemtum = {var: cp.zeros_like(par_gpu[var]) for var in par_gpu.keys()}
for i in range(int(epochs)):
for batch in self.iterate_minibatches(X, y, batch_size):
X_batch, y_batch = batch
Z = cp.random.binomial(1, p, size=X_batch.shape)
X_batch_dropout = cp.multiply(X_batch, Z)
grad_p = self.model.grad(par_gpu,
X_train=X_batch_dropout,
y_train=y_batch)
for var in par_gpu.keys():
momemtum[var] = gamma * momemtum[
var] + -self.step_size * grad_p[var]
par_gpu[var] += momemtum[var]
loss_val[i] = self.model.negative_log_posterior(par_gpu,
X_train=X_batch,
y_train=y_batch)
if verbose and (i % (epochs / 10) == 0):
print('loss: {0:.4f}'.format(cp.asnumpy(loss_val[i])))
return par_gpu, loss_val
def get_square_sampling_probas(attractivity_cells,
square_ids_cells,
coords_squares,
dscale=1):
# compute sum attractivities in squares
sum_attractivity_squares, unique_squares = sum_by_group(
values=attractivity_cells, groups=square_ids_cells)
# Compute distances between all squares and squares having sum_attractivity > 0
mask_attractivity = (sum_attractivity_squares > 0)
eligible_squares = unique_squares[mask_attractivity]
sum_attractivity_squares = sum_attractivity_squares[mask_attractivity]
# Compute distance between cells, add `intra_square_dist` for average intra cell distance
inter_square_dists = cdist(coords_squares).astype(cp.float32)
inter_square_dists = inter_square_dists[:, eligible_squares]
square_sampling_probas = cp.multiply(inter_square_dists, -dscale)
square_sampling_probas = cp.exp(square_sampling_probas)
square_sampling_probas *= sum_attractivity_squares[
None, :] # row-wise multiplication
square_sampling_probas /= cp.linalg.norm(square_sampling_probas,
ord=1,
axis=1,
keepdims=True)
square_sampling_probas = square_sampling_probas.astype(cp.float32)
return square_sampling_probas
def inner(a, b):
"""Returns the inner product of two arrays.
It uses the last axis of each argument to take sum product.
Args:
a (cupy.ndarray): The first argument.
b (cupy.ndarray): The second argument.
Returns:
cupy.ndarray: The inner product of ``a`` and ``b``.
.. seealso:: :func:`numpy.inner`
"""
a_ndim = a.ndim
b_ndim = b.ndim
if a_ndim == 0 or b_ndim == 0:
return cupy.multiply(a, b)
a_axis = a_ndim - 1
b_axis = b_ndim - 1
if a.shape[-1] != b.shape[-1]:
raise ValueError('Axis dimension mismatch')
if a_axis:
a = cupy.rollaxis(a, a_axis, 0)
if b_axis:
b = cupy.rollaxis(b, b_axis, 0)
ret_shape = a.shape[1:] + b.shape[1:]
k = a.shape[0]
n = a.size // k
m = b.size // k
return _tensordot_core(a, b, None, n, m, k, ret_shape)
def train(self):
# clear grads
self.q_func.zerograds()
# pull tuples from memory pool
batch_tuples = self.replay.pull(Config.batch_size)
if not len(batch_tuples):
return
# stack inputs
cur_x = [self.env.getX(t.state) for t in batch_tuples]
next_x = [self.env.getX(t.next_state) for t in batch_tuples]
# merge inputs into one array
if Config.gpu:
cur_x = [cupy.expand_dims(t, 0) for t in cur_x]
cur_x = cupy.concatenate(cur_x, 0)
next_x = [cupy.expand_dims(t, 0) for t in next_x]
next_x = cupy.concatenate(next_x, 0)
else:
cur_x = np.stack(cur_x)
next_x = np.stack(next_x)
# get cur outputs
cur_output = self.QFunc(self.q_func, cur_x)
# get next outputs, NOT target
next_output = self.QFunc(self.q_func, next_x)
# choose next action for each output
next_action = [
self.env.getBestAction(
o.data,
[t.next_state for t in batch_tuples]
) for o in next_output # for each head in Model
]
# get next outputs, target
next_output = self.QFunc(self.target_q_func, next_x)
# clear err of tuples
for t in batch_tuples:
t.err = 0.
# store err count
err_count_list = [0.] * len(batch_tuples)
# compute grad's weights
weights = np.array([t.P for t in batch_tuples], np.float32)
if Config.gpu:
weights = cuda.to_gpu(weights)
if self.replay.getPoolSize():
weights *= self.replay.getPoolSize()
weights = weights ** -Config.beta
weights /= weights.max()
if Config.gpu:
weights = cupy.expand_dims(weights, 1)
else:
weights = np.expand_dims(weights, 1)
# update beta
Config.beta = min(1, Config.beta + Config.beta_add)
# compute grad for each head
for k in range(Config.K):
if Config.gpu:
cur_output[k].grad = cupy.zeros_like(cur_output[k].data)
else:
cur_output[k].grad = np.zeros_like(cur_output[k].data)
# compute grad from each tuples
for i in range(len(batch_tuples)):
if batch_tuples[i].mask[k]:
cur_action_value = \
cur_output[k].data[i][batch_tuples[i].action].tolist()
reward = batch_tuples[i].reward
next_action_value = \
next_output[k].data[i][next_action[k][i]].tolist()
target_value = reward
# if not empty position, not terminal state
if batch_tuples[i].next_state.in_game:
target_value += Config.gamma * next_action_value
loss = cur_action_value - target_value
cur_output[k].grad[i][batch_tuples[i].action] = 2 * loss
# count err
if cur_action_value:
batch_tuples[i].err += abs(loss / cur_action_value)
err_count_list[i] += 1
# multiply weights with grad and clip
if Config.gpu:
cur_output[k].grad = cupy.multiply(
cur_output[k].grad, weights)
cur_output[k].grad = cupy.clip(cur_output[k].grad, -1, 1)
else:
cur_output[k].grad = np.multiply(
cur_output[k].grad, weights)
cur_output[k].grad = np.clip(cur_output[k].grad, -1, 1)
# backward
cur_output[k].backward()
# adjust grads of shared
for param in self.q_func.shared.params():
param.grad /= Config.K
# update params
self.optimizer.update()
# avg err
for i in range(len(batch_tuples)):
if err_count_list[i] > 0:
batch_tuples[i].err /= err_count_list[i]
self.replay.merge(Config.alpha)
return np.mean([t.err for t in batch_tuples])
def tensordot(a, b, axes=2):
"""Returns the tensor dot product of two arrays along specified axes.
This is equivalent to compute dot product along the specified axes which
are treated as one axis by reshaping.
Args:
a (cupy.ndarray): The first argument.
b (cupy.ndarray): The second argument.
axes:
- If it is an integer, then ``axes`` axes at the last of ``a`` and
the first of ``b`` are used.
- If it is a pair of sequences of integers, then these two
sequences specify the list of axes for ``a`` and ``b``. The
corresponding axes are paired for sum-product.
out (cupy.ndarray): Output array.
Returns:
cupy.ndarray: The tensor dot product of ``a`` and ``b`` along the
axes specified by ``axes``.
.. seealso:: :func:`numpy.tensordot`
"""
a_ndim = a.ndim
b_ndim = b.ndim
if a_ndim == 0 or b_ndim == 0:
if axes != 0 and axes != ((), ()):
raise ValueError('An input is zero-dim while axes has dimensions')
return cupy.multiply(a, b)
if isinstance(axes, collections.Sequence):
if len(axes) != 2:
raise ValueError('Axes must consist of two arrays.')
a_axes, b_axes = axes
if numpy.isscalar(a_axes):
a_axes = a_axes,
if numpy.isscalar(b_axes):
b_axes = b_axes,
else:
a_axes = tuple(six.moves.range(a_ndim - axes, a_ndim))
b_axes = tuple(six.moves.range(axes))
sum_ndim = len(a_axes)
if sum_ndim != len(b_axes):
raise ValueError('Axes length mismatch')
for a_axis, b_axis in zip(a_axes, b_axes):
if a.shape[a_axis] != b.shape[b_axis]:
raise ValueError('Axis dimension mismatch')
# Make the axes non-negative
a = _move_axes_to_head(a, [axis % a_ndim for axis in a_axes])
b = _move_axes_to_head(b, [axis % b_ndim for axis in b_axes])
ret_shape = a.shape[sum_ndim:] + b.shape[sum_ndim:]
k = internal.prod(a.shape[:sum_ndim])
n = a.size // k
m = b.size // k
return _tensordot_core(a, b, None, n, m, k, ret_shape)
def _tensordot_core(a, b, out, n, m, k, ret_shape):
ret_dtype = a.dtype.char
if ret_dtype != b.dtype.char:
ret_dtype = numpy.find_common_type((ret_dtype, b.dtype), ()).char
# Cast to float32 or float64
if ret_dtype == 'f' or ret_dtype == 'd':
dtype = ret_dtype
else:
dtype = numpy.find_common_type((ret_dtype, 'f'), ()).char
a = a.astype(dtype, copy=False)
b = b.astype(dtype, copy=False)
if not a.size or not b.size:
if a.size or b.size:
raise ValueError('cannot dot zero-sized and non-zero-sized arrays')
if out is None:
return cupy.zeros(ret_shape, dtype=ret_dtype)
else:
out.fill(0)
return out
if out is None:
out = cupy.empty(ret_shape, dtype)
if dtype == ret_dtype:
ret = out
else:
ret = cupy.empty(ret_shape, ret_dtype)
else:
ret = out
if out.dtype != dtype:
out = cupy.empty(ret_shape, dtype)
# It copies the operands if needed
if a.shape != (k, n):
a = cupy.reshape(a, (k, n))
if b.shape != (k, m):
b = cupy.reshape(b, (k, m))
c = out
if c.shape != (n, m):
c = c.view()
c.shape = (n, m)
# Be careful that cuBLAS uses the FORTRAN-order matrix representation.
if k == 1:
if n == 1:
# Scalar-vector product
cupy.multiply(a, b, c)
elif m == 1:
# Scalar-vector product
cupy.multiply(a.T, b, c)
else:
# Outer product A^T * B
# c is C-contiguous while cuBLAS requires F-contiguous arrays, so
# we compute C^T = B^T * A here.
handle = cuda.Device().cublas_handle
c.fill(0)
a, inca = _to_cublas_vector(a, 1)
b, incb = _to_cublas_vector(b, 1)
if dtype == 'f':
ger = cublas.sger
elif dtype == 'd':
ger = cublas.dger
ger(handle, m, n, 1, b.data.ptr, incb, a.data.ptr, inca,
c.data.ptr, m)
if dtype != ret_dtype:
elementwise.copy(out, ret)
return ret
handle = cuda.Device().cublas_handle
if n == 1:
if m == 1:
# Inner product
a, inca = _to_cublas_vector(a, 0)
b, incb = _to_cublas_vector(b, 0)
mode = cublas.getPointerMode(handle)
cublas.setPointerMode(handle,
cublas.CUBLAS_POINTER_MODE_DEVICE)
if dtype == 'f':
dot = cublas.sdot
elif dtype == 'd':
dot = cublas.ddot
try:
dot(handle, k, a.data.ptr, inca, b.data.ptr, incb, c.data.ptr)
finally:
cublas.setPointerMode(handle, mode)
else:
# Matrix-vector product B^T * A
a, inca = _to_cublas_vector(a, 0)
b, transb, ldb = _mat_to_cublas_contiguous(b, 1)
if transb:
# gemv requires (m, k) as the original matrix dimensions
# rather than the transposed dimensions.
m, k = k, m
if dtype == 'f':
gemv = cublas.sgemv
elif dtype == 'd':
gemv = cublas.dgemv
gemv(handle, transb, m, k, 1, b.data.ptr, ldb, a.data.ptr, inca,
0, c.data.ptr, 1)
elif m == 1:
# Matrix-vector product A^T * B
a, transa, lda = _mat_to_cublas_contiguous(a, 1)
b, incb = _to_cublas_vector(b, 0)
if transa:
# gemv requires (n, k) as the original matrix dimensions rather
# than the transposed dimensions.
n, k = k, n
if dtype == 'f':
gemv = cublas.sgemv
elif dtype == 'd':
gemv = cublas.dgemv
gemv(handle, transa, n, k, 1, a.data.ptr, lda, b.data.ptr, incb, 0,
c.data.ptr, 1)
else:
# Matrix-Matrix product A^T * B
# c is C-contiguous while cuBLAS assumes F-contiguous inputs, so we
# compute C^T = B^T * A here.
a, transa, lda = _mat_to_cublas_contiguous(a, 0)
b, transb, ldb = _mat_to_cublas_contiguous(b, 1)
if dtype == 'f':
gemm = cublas.sgemm
elif dtype == 'd':
gemm = cublas.dgemm
gemm(handle, transb, transa, m, n, k, 1, b.data.ptr, ldb, a.data.ptr,
lda, 0, c.data.ptr, m)
if dtype != ret_dtype:
elementwise.copy(out, ret)
return ret
def tensordot(a, b, axes=2, out=None):
"""Returns the tensor dot product of two arrays along specified axes.
This is equivalent to compute dot product along the specified axes which
are treated as one axis by reshaping.
Args:
a (cupy.ndarray): The first argument.
b (cupy.ndarray): The second argument.
axes:
- If it is an integer, then ``axes`` axes at the last of ``a`` and
the first of ``b`` are used.
- If it is a pair of sequences of integers, then these two
sequences specify the list of axes for ``a`` and ``b``. The
corresponding axes are paired for sum-product.
out (cupy.ndarray): Output array.
Returns:
cupy.ndarray: The tensor dot product of ``a`` and ``b`` along the
axes specified by ``axes``.
.. seealso:: :func:`numpy.tensordot`
"""
if a.ndim == 0 or b.ndim == 0:
if axes != 0 and axes != ((), ()):
raise ValueError('An input is zero-dim while axes has dimensions')
return cupy.multiply(a, b, out=out)
ret_dtype = numpy.find_common_type([a.dtype, b.dtype], [])
# Cast to float32 or float64
dtype = numpy.find_common_type([a.dtype, b.dtype, 'f'], [])
a = a.astype(dtype, copy=False)
b = b.astype(dtype, copy=False)
if a.dtype.type == numpy.float32:
dot = cublas.sdot
gemv = cublas.sgemv
ger = cublas.sger
gemm = cublas.sgemm
elif a.dtype.type == numpy.float64:
dot = cublas.ddot
gemv = cublas.dgemv
ger = cublas.dger
gemm = cublas.dgemm
if numpy.isscalar(axes):
axes = [list(six.moves.range(a.ndim - axes, a.ndim)),
list(six.moves.range(axes))]
else:
axes = list(axes)
if numpy.isscalar(axes[0]):
axes[0] = (axes[0],)
if numpy.isscalar(axes[1]):
axes[1] = (axes[1],)
if len(axes) != 2:
raise ValueError('Axes must consist of two arrays.')
if len(axes[0]) != len(axes[1]):
raise ValueError('Axes length mismatch')
for a_axis, b_axis in zip(*axes):
if not (-a.ndim <= a_axis < a.ndim and
-b.ndim <= b_axis < b.ndim):
raise IndexError('Axis overrun')
if a.shape[a_axis] != b.shape[b_axis]:
raise ValueError('Axis dimension mismatch')
# Make the axes non-negative
axes = (tuple(axis % a.ndim for axis in axes[0]),
tuple(axis % b.ndim for axis in axes[1]))
sum_ndim = len(axes[0])
a = _move_axes_to_head(a, axes[0])
b = _move_axes_to_head(b, axes[1])
m = internal.prod(b.shape[sum_ndim:])
n = internal.prod(a.shape[sum_ndim:])
ret_shape = a.shape[sum_ndim:] + b.shape[sum_ndim:]
if out is not None:
if out.size != internal.prod(ret_shape):
raise ValueError('Output array has an invalid size')
if not out.flags.c_contiguous:
raise ValueError('Output array must be C-contiguous')
if 0 in a.shape or 0 in b.shape:
if 0 not in a.shape or 0 not in b.shape:
raise ValueError('cannot dot zero-sized and non-zero-sized arrays')
if out is None:
return cupy.zeros(ret_shape, dtype=ret_dtype)
else:
out.fill(0)
return out
if out is None:
out = cupy.empty(ret_shape, dtype=dtype)
if dtype == ret_dtype:
ret = out
else:
ret = cupy.empty(ret_shape, dtype=ret_dtype)
else:
ret = out
if out.dtype != dtype:
out = cupy.empty(ret_shape, dtype=dtype)
k = a.size // n
# It copies the operands if needed
a = a.reshape(k, n)
b = b.reshape(k, m)
c = out.view()
c.shape = (n, m)
# Be careful that cuBLAS uses the FORTRAN-order matrix representation.
handle = cuda.Device().cublas_handle
if k == 1:
if n == 1 or m == 1:
# Scalar-vector product
cupy.multiply(a.T, b, c)
else:
# Outer product A^T * B
# c is C-contiguous while cuBLAS requires F-contiguous arrays, so
# we compute C^T = B^T * A here.
c.fill(0)
a, inca = _to_cublas_vector(a, 1)
b, incb = _to_cublas_vector(b, 1)
ger(handle, m, n, 1, b._fptr, incb, a._fptr, inca, c._fptr, m)
elif n == 1:
if m == 1:
# Inner product
a, inca = _to_cublas_vector(a, 0)
b, incb = _to_cublas_vector(b, 0)
mode = cublas.getPointerMode(handle)
cublas.setPointerMode(handle,
cublas.CUBLAS_POINTER_MODE_DEVICE)
try:
dot(handle, k, a._fptr, inca, b._fptr, incb, c._fptr)
finally:
cublas.setPointerMode(handle, mode)
else:
# Matrix-vector product B^T * A
a, inca = _to_cublas_vector(a, 1)
b, transb, ldb = _mat_to_cublas_contiguous(b.T)
if transb:
# gemv requires (m, k) as the original matrix dimensions
# rather than the transposed dimensions.
m, k = k, m
gemv(handle, transb, m, k, 1, b._fptr, ldb, a._fptr, inca,
0, c._fptr, 1)
elif m == 1:
# Matrix-vector product A^T * B
a, transa, lda = _mat_to_cublas_contiguous(a.T)
b, incb = _to_cublas_vector(b, 1)
if not transa:
# gemv requires (n, k) as the original matrix dimensions rather
# than the transposed dimensions.
n, k = k, n
gemv(handle, transa, n, k, 1, a._fptr, lda, b._fptr, incb, 0, c._fptr,
1)
else:
# Matrix-Matrix product A^T * B
# c is C-contiguous while cuBLAS assumes F-contiguous inputs, so we
# compute C^T = B^T * A here.
a, transa, lda = _mat_to_cublas_contiguous(a)
b, transb, ldb = _mat_to_cublas_contiguous(b.T)
gemm(handle, transb, transa, m, n, k, 1, b._fptr, ldb, a._fptr,
lda, 0, c._fptr, m)
if dtype != ret_dtype:
elementwise.copy(out, ret)
return ret
