def check_array(self, array, shape, dtype, copy=True):
"""
Check that a given array is compatible with the FFTW plans,
in terms of alignment and data type.
If the provided array does not meet any of the checks, a new array
is returned.
"""
if array.shape != shape:
raise ValueError("Invalid data shape: expected %s, got %s" %
(shape, array.shape)
)
if array.dtype != dtype:
raise ValueError("Invalid data type: expected %s, got %s" %
(dtype, array.dtype)
)
if self.check_alignment and not(pyfftw.is_byte_aligned(array)):
array2 = pyfftw.zeros_aligned(self.shape, dtype=self.dtype_in)
np.copyto(array2, array)
else:
if copy:
array2 = np.copy(array)
else:
array2 = array
return array2
def copyto(dst, src):
"""Copies the elements of an ndarray to those of another one.
This function can copy the CPU/GPU arrays to the destination arrays on
another device.
Args:
dst (`numpy.ndarray`, `cupy.ndarray` or `ideep4py.mdarray`):
Destination array.
src (`numpy.ndarray`, `cupy.ndarray` or `ideep4py.mdarray`):
Source array.
"""
if isinstance(dst, numpy.ndarray):
numpy.copyto(dst, _cpu._to_cpu(src))
elif isinstance(dst, intel64.mdarray):
intel64.ideep.basic_copyto(
dst, _cpu._to_cpu(src))
elif isinstance(dst, cuda.ndarray):
if isinstance(src, chainer.get_cpu_array_types()):
src = numpy.asarray(src)
if dst.flags.c_contiguous or dst.flags.f_contiguous:
dst.set(src)
else:
cuda.cupy.copyto(dst, cuda.to_gpu(src, device=dst.device))
elif isinstance(src, cuda.ndarray):
cuda.cupy.copyto(dst, src)
else:
raise TypeError('cannot copy from non-array object of type {}'
.format(type(src)))
else:
raise TypeError('cannot copy to non-array object of type {}'.format(
type(dst)))
def laplacian(grid, out):
np.copyto(out, grid)
out *= -4
out += np.roll(grid, +1, 0)
out += np.roll(grid, -1, 0)
out += np.roll(grid, +1, 1)
out += np.roll(grid, -1, 1)
def guess_data_type(orig_values, namask=None):
"""
Use heuristics to guess data type.
"""
valuemap, values = None, orig_values
is_discrete = is_discrete_values(orig_values)
if is_discrete:
valuemap = sorted(is_discrete)
coltype = DiscreteVariable
else:
# try to parse as float
orig_values = np.asarray(orig_values)
if namask is None:
namask = isnastr(orig_values)
values = np.empty_like(orig_values, dtype=float)
values[namask] = np.nan
try:
np.copyto(values, orig_values, where=~namask, casting="unsafe")
except ValueError:
tvar = TimeVariable('_')
try:
values[~namask] = [tvar.parse(i) for i in orig_values[~namask]]
except ValueError:
coltype = StringVariable
# return original_values
values = orig_values
else:
coltype = TimeVariable
else:
coltype = ContinuousVariable
return valuemap, values, coltype
def _copyto(a, val, mask):
"""
Replace values in `a` with NaN where `mask` is True. This differs from
copyto in that it will deal with the case where `a` is a numpy scalar.
Parameters
----------
a : ndarray or numpy scalar
Array or numpy scalar some of whose values are to be replaced
by val.
val : numpy scalar
Value used a replacement.
mask : ndarray, scalar
Boolean array. Where True the corresponding element of `a` is
replaced by `val`. Broadcasts.
Returns
-------
res : ndarray, scalar
Array with elements replaced or scalar `val`.
"""
if isinstance(a, np.ndarray):
np.copyto(a, val, where=mask, casting='unsafe')
else:
a = a.dtype.type(val)
return a
def fdtd(input_grid, steps):
grid = input_grid.copy()
old_grid = np.zeros_like(input_grid)
previous_grid = np.zeros_like(input_grid)
l_x = grid.shape[0]
l_y = grid.shape[1]
for i in range(steps):
np.copyto(previous_grid, old_grid)
np.copyto(old_grid, grid)
for x in range(l_x):
for y in range(l_y):
grid[x,y] = 0.0
if 0 < x+1 < l_x:
grid[x,y] += old_grid[x+1,y]
if 0 < x-1 < l_x:
grid[x,y] += old_grid[x-1,y]
if 0 < y+1 < l_y:
grid[x,y] += old_grid[x,y+1]
if 0 < y-1 < l_y:
grid[x,y] += old_grid[x,y-1]
grid[x,y] /= 2.0
grid[x,y] -= previous_grid[x,y]
return grid
def testTensorAccessor(self):
"""Check that tensor returns a reference."""
array_ref = self.interpreter.tensor(self.input0)
np.copyto(array_ref(), self.initial_data)
self.assertAllEqual(array_ref(), self.initial_data)
self.assertAllEqual(
self.interpreter.get_tensor(self.input0), self.initial_data)
def acartesian2(*arrays):
"""Array cartesian product in 2d
Produces a new ndarray that has the cartesian product of every row in the
input arrays. The number of columns is the sum of the number of columns in
each input. The number of rows is the product of the number of rows in each
input.
Arguments
---------
*arrays : [ndarray (xi, s)]
"""
rows = prod(a.shape[0] for a in arrays)
columns = sum(a.shape[1] for a in arrays)
dtype = arrays[0].dtype # should always have at least one role
assert all(a.dtype == dtype for a in arrays), \
"all arrays must have the same dtype"
result = np.zeros((rows, columns), dtype)
pre_row = 1
post_row = rows
pre_column = 0
for array in arrays:
length, width = array.shape
post_row //= length
post_column = pre_column + width
view = result[:, pre_column:post_column]
view.shape = (pre_row, -1, post_row, width)
np.copyto(view, array[:, None])
pre_row *= length
pre_column = post_column
return result
def __call__(self, key, value):
key = self.path + key.lstrip('/')
if not self.strict and key not in self.npz:
return value
if isinstance(self.ignore_names, (tuple, list)):
ignore_names = self.ignore_names
else:
ignore_names = (self.ignore_names,)
for ignore_name in ignore_names:
if isinstance(ignore_name, str):
if key == ignore_name:
return value
elif callable(ignore_name):
if ignore_name(key):
return value
else:
raise ValueError(
'ignore_names needs to be a callable, string or '
'list of them.')
dataset = self.npz[key]
if dataset[()] is None:
return None
if value is None:
return dataset
elif isinstance(value, numpy.ndarray):
numpy.copyto(value, dataset)
elif isinstance(value, cuda.ndarray):
value.set(numpy.asarray(dataset))
else:
value = type(value)(numpy.asarray(dataset))
return value
def extract_features(annotation, image_size=(64, 64)):
n = len(annotation)
for i, a in enumerate(annotation):
print_progress_bar(i, n)
image_path = a["image"]
label = a["label"]
image = cv2.imread(image_path)
if image is None:
continue
image = cv2.resize(image, image_size, image)
image_channels = cv2.split(image)
for channel_idx, channel in enumerate(image_channels):
np.copyto(net.blobs["data"].data[0, channel_idx, :, :], channel)
# image = np.dstack(cv2.split(image))
# np.copyto(net.blobs["data"].data, image)
# net.blobs["data"].data = image
output_blobs = net.forward(end="conv1", blobs=["conv1", ])
channels_num = output_blobs["conv1"].shape[1]
channels = [output_blobs["conv1"][0, i, :, :] for i in range(channels_num)]
features = cv2.merge(channels)
output_dir = join(args.features_dir, "positives" if label else "negatives")
if not isdir(output_dir):
mkdir(output_dir)
feature_map_path = join(output_dir, splitext(basename(image_path))[0] + ".pkl")
pkl.dump(features, file(feature_map_path, "w"))
stop_progress_bar()
def npArrayToReadOnlySharedArray(npArray):
'''Returns a shared memory array for a numpy array. Used to reduce memory footprint when passing parameters to multiprocess pools'''
SharedBase = multiprocessing.sharedctypes.RawArray(ctypes.c_float, npArray.shape[0] * npArray.shape[1])
SharedArray = np.ctypeslib.as_array(SharedBase)
SharedArray = SharedArray.reshape(npArray.shape)
np.copyto(SharedArray, npArray)
return SharedArray
def embed(cover,secret,pos,skip):
file=open("in.txt","w")
multiple=False
coverMatrix=pgm_to_mat(cover)
secretMatrix=pgm_to_mat(secret)
stegoMatrix=np.zeros(np.shape(coverMatrix), dtype=np.complex_)
np.copyto(stegoMatrix,coverMatrix)
dummy=""
if(skip<1):
skip=1
multiple=True
for a in range(0,len(secretMatrix)):
for b in range(0,len(secretMatrix)):
dummy+=np.binary_repr(secretMatrix[a][b],width=8)
#file.write(np.binary_repr(secretMatrix[a][b],width=8)+"\n")
index=0
for a in range(0,len(stegoMatrix)*len(stegoMatrix),skip):
rown=int(a % len(stegoMatrix))
coln=int(a / len(stegoMatrix))
if(index>=len(dummy)):
break
stegoMatrix[coln][rown] = ( int(coverMatrix[coln][rown]) & ~(1 << hash(coln,rown,pos) )) | (int(dummy[index],2) << hash(coln,rown,pos))
index += 1
if(multiple):
stegoMatrix[coln][rown] = (int(stegoMatrix[coln][rown]) & ~(1 << (3-hash(coln, rown, pos)))) | ( int(dummy[index], 2) << (3-hash(coln, rown, pos)))
index += 1
file.write(np.binary_repr(int(stegoMatrix[coln][rown]), 8) + "\n")
return stegoMatrix
def binary_to_net(weights, spm_stream, ind_stream, codebook, num_nz):
bits = np.log2(codebook.size)
if bits == 4:
slots = 2
elif bits == 8:
slots = 1
else:
print "Not impemented,", bits
sys.exit()
code = np.zeros(weights.size, np.uint8)
# Recover from binary stream
spm = np.zeros(num_nz, np.uint8)
ind = np.zeros(num_nz, np.uint8)
if slots == 2:
spm[np.arange(0, num_nz, 2)] = spm_stream % (2**4)
spm[np.arange(1, num_nz, 2)] = spm_stream / (2**4)
else:
spm = spm_stream
ind[np.arange(0, num_nz, 2)] = ind_stream% (2**4)
ind[np.arange(1, num_nz, 2)] = ind_stream/ (2**4)
# Recover the matrix
ind = np.cumsum(ind+1)-1
code[ind] = spm
data = np.reshape(codebook[code], weights.shape)
np.copyto(weights, data)
def laplacian(grid, out):
copyto(out, grid)
multiply(out, -4.0, out)
roll_add(grid, +1, 0, out)
roll_add(grid, -1, 0, out)
roll_add(grid, +1, 1, out)
roll_add(grid, -1, 1, out)
def learn_parameters(adj_list, q, c_max, nb_iterations, crit_infer = 0.2, crit_learn = 0.2, tmax_infer = 12, tmax_learn = 8): """ Learns the true group parameters of a given graph, and returns the optimal group assignment given by the belief propagation algorithm """ # Number of nodes on the graph N = len(adj_list) # Optimal values given by the algorithm f_min = 0 groups_opt = np.zeros(N, dtype = np.int8) for _ in range(nb_iterations): # Random initialization of each group's size n = np.random.rand(q) n /= np.sum(n) # Random initialization of the edge matrix c = np.random.rand(q, q) for i in range(q - 1): for j in range(i + 1, q): c[j, i] = c[i, j] c *= c_max # Application of the BP_learning algorithm for these initialized values groups, f_BP = BP_learning(q, n, c, adj_list, crit_infer, crit_learn, tmax_infer, tmax_learn) # Updates the optimal values found if f_BP < f_min: f_min = f_BP np.copyto(groups_opt, groups) # Returns the optimal group assignment found return groups_opt
def draw_mask_on_image_array(image, mask, color='red', alpha=0.4):
"""Draws mask on an image.
Args:
image: uint8 numpy array with shape (img_height, img_height, 3)
mask: a uint8 numpy array of shape (img_height, img_height) with
values between either 0 or 1.
color: color to draw the keypoints with. Default is red.
alpha: transparency value between 0 and 1. (default: 0.4)
Raises:
ValueError: On incorrect data type for image or masks.
"""
if image.dtype != np.uint8:
raise ValueError('`image` not of type np.uint8')
if mask.dtype != np.uint8:
raise ValueError('`mask` not of type np.uint8')
if np.any(np.logical_and(mask != 1, mask != 0)):
raise ValueError('`mask` elements should be in [0, 1]')
if image.shape[:2] != mask.shape:
raise ValueError('The image has spatial dimensions %s but the mask has '
'dimensions %s' % (image.shape[:2], mask.shape))
rgb = ImageColor.getrgb(color)
pil_image = Image.fromarray(image)
solid_color = np.expand_dims(
np.ones_like(mask), axis=2) * np.reshape(list(rgb), [1, 1, 3])
pil_solid_color = Image.fromarray(np.uint8(solid_color)).convert('RGBA')
pil_mask = Image.fromarray(np.uint8(255.0*alpha*mask)).convert('L')
pil_image = Image.composite(pil_solid_color, pil_image, pil_mask)
np.copyto(image, np.array(pil_image.convert('RGB')))
def draw_bounding_box_on_image_array(image,
ymin,
xmin,
ymax,
xmax,
color='red',
thickness=4,
display_str_list=(),
use_normalized_coordinates=True):
"""Adds a bounding box to an image (numpy array).
Bounding box coordinates can be specified in either absolute (pixel) or
normalized coordinates by setting the use_normalized_coordinates argument.
Args:
image: a numpy array with shape [height, width, 3].
ymin: ymin of bounding box.
xmin: xmin of bounding box.
ymax: ymax of bounding box.
xmax: xmax of bounding box.
color: color to draw bounding box. Default is red.
thickness: line thickness. Default value is 4.
display_str_list: list of strings to display in box
(each to be shown on its own line).
use_normalized_coordinates: If True (default), treat coordinates
ymin, xmin, ymax, xmax as relative to the image. Otherwise treat
coordinates as absolute.
"""
image_pil = Image.fromarray(np.uint8(image)).convert('RGB')
draw_bounding_box_on_image(image_pil, ymin, xmin, ymax, xmax, color,
thickness, display_str_list,
use_normalized_coordinates)
np.copyto(image, np.array(image_pil))
def _applyTo(self, image):
noise_array = np.empty(np.prod(image.array.shape), dtype=float)
noise_array.reshape(image.array.shape)[:,:] = image.array
# cf. PoissonNoise._applyTo function
frac_sky = self.sky_level - image.dtype(self.sky_level) # 0 if dtype = float
int_sky = self.sky_level - frac_sky
if self.sky_level != 0.:
noise_array += self.sky_level
# First add the poisson noise from the signal + sky:
if self.gain > 0.:
noise_array *= self.gain # convert to electrons
noise_array = noise_array.clip(0.)
# The noise_image now has the expectation values for each pixel with the sky added.
self._pd.generate_from_expectation(noise_array)
# Subtract off the sky, since we don't want it in the final image.
noise_array /= self.gain
# Now add the read noise:
if self.read_noise > 0.:
self._gd.clearCache()
self._gd.add_generate(noise_array)
if frac_sky != 0.:
noise_array -= frac_sky
np.copyto(image.array, noise_array.reshape(image.array.shape), casting='unsafe')
if int_sky != 0.:
image -= int_sky
def draw_bounding_boxes_on_image_array(image,
boxes,
color='red',
thickness=4,
display_str_list_list=()):
"""Draws bounding boxes on image (numpy array).
Args:
image: a numpy array object.
boxes: a 2 dimensional numpy array of [N, 4]: (ymin, xmin, ymax, xmax).
The coordinates are in normalized format between [0, 1].
color: color to draw bounding box. Default is red.
thickness: line thickness. Default value is 4.
display_str_list_list: list of list of strings.
a list of strings for each bounding box.
The reason to pass a list of strings for a
bounding box is that it might contain
multiple labels.
Raises:
ValueError: if boxes is not a [N, 4] array
"""
image_pil = Image.fromarray(image)
draw_bounding_boxes_on_image(image_pil, boxes, color, thickness,
display_str_list_list)
np.copyto(image, np.array(image_pil))
def conditional_logsumexp(where, axis):
masked = -np.ones(a.shape) * np.inf
np.copyto(masked, a, where = where)
masked_sum = logsumexp(masked, axis = axis)
#np.copyto(masked_sum, -np.ones(masked_sum.shape) * np.inf, where = np.isnan(masked_sum))
np.place(masked_sum, np.isnan(masked_sum), -np.inf)
return masked_sum
def _applyTo(self, image):
noise_array = np.empty(np.prod(image.array.shape), dtype=float)
noise_array.reshape(image.array.shape)[:,:] = image.array
# Minor subtlety for integer images. It's a bit more consistent to convert to an
# integer with the sky still added and then subtract off the sky. But this isn't quite
# right if the sky has a fractional part. So only subtract off the integer part of the
# sky at the end. For float images, you get the same answer either way, so it doesn't
# matter.
frac_sky = self.sky_level - image.dtype(self.sky_level)
int_sky = self.sky_level - frac_sky
if self.sky_level != 0.:
noise_array += self.sky_level
# Make sure no negative values
noise_array = noise_array.clip(0.)
# The noise_image now has the expectation values for each pixel with the sky added.
self._pd.generate_from_expectation(noise_array)
# Subtract off the sky, since we don't want it in the final image.
if frac_sky != 0.:
noise_array -= frac_sky
# Noise array is now the correct value for each pixel.
np.copyto(image.array, noise_array.reshape(image.array.shape), casting='unsafe')
if int_sky != 0.:
image -= int_sky
def test_array_maskna_to_nomask():
# Assignment from an array with NAs to a non-masked array,
# excluding the NAs with a mask
a = np.array([[2,np.NA,5],[1,6,np.NA]], maskna=True)
mask = np.array([[1,0,0],[1,1,0]], dtype='?')
badmask = np.array([[1,0,0],[0,1,1]], dtype='?')
expected = np.array([[2,1,2],[1,6,5]])
# With masked indexing
b = np.arange(6).reshape(2,3)
b[mask] = a[mask]
assert_array_equal(b, expected)
# With copyto
b = np.arange(6).reshape(2,3)
np.copyto(b, a, where=mask)
assert_array_equal(b, expected)
# With masked indexing
b = np.arange(6).reshape(2,3)
def asn():
b[badmask] = a[badmask]
assert_raises(ValueError, asn)
# With copyto
b = np.arange(6).reshape(2,3)
assert_raises(ValueError, np.copyto, b, a, where=badmask)
def apply_qt_elements_filtering(image):
#
# apply bilateral filter on IMAGE to smooth colors while keeping edges
cv2.bilateralFilter(UtilityOperations.crop_to_720p(image), 3, 255, 50,
dst=MultiprocessOperations.shared_memory_image)
#
# crop elements from IMAGE
MultiprocessOperations.wait_for_element_cropping()
#
# upload RED channels to GPU
TheanoOperations.__shared_red.set_value(
MultiprocessOperations.shared_memory_red_channel, borrow=True)
#
# upload GREEN channels to GPU
TheanoOperations.__shared_green.set_value(
MultiprocessOperations.shared_memory_green_channel, borrow=True)
#
# upload BLUE channels to GPU
TheanoOperations.__shared_blue.set_value(
MultiprocessOperations.shared_memory_blue_channel, borrow=True)
#
# download FILTERING result from GPU
np.copyto(MultiprocessOperations.shared_memory_qt_filtered_elements,
TheanoOperations.__apply_binary_filtering)
#
# apply IMAGE threshold CLASSIFICATION
MultiprocessOperations.wait_for_qt_image_classification()
return MultiprocessOperations.shared_memory_qt_filtered_elements
def _load_flat_grad(self, flat_grad):
start = 0
for g in self._grad_buffers:
size = g.size
np.copyto(g, np.reshape(flat_grad[start:start + size], g.shape))
start += size
return
def serialize(self, serializer):
"""Serializes the link object.
Args:
serializer (~chainer.AbstractSerializer): Serializer object.
"""
d = self.__dict__
for name in self._params:
serializer(name, d[name].data)
for name in self._persistent:
d[name] = serializer(name, d[name])
if (self.has_uninitialized_params and
isinstance(serializer, chainer.serializer.Serializer)):
raise ValueError("uninitialized parameters cannot be serialized")
for name in self._uninitialized_params.copy():
# Note: There should only be uninitialized parameters
# during deserialization.
initialized_value = serializer(name, None)
self.add_param(name, initialized_value.shape)
uninitialized_value = d[name].data
if isinstance(uninitialized_value, numpy.ndarray):
numpy.copyto(uninitialized_value, initialized_value)
elif isinstance(uninitialized_value, cuda.ndarray):
uninitialized_value.set(numpy.asarray(initialized_value))
def lieberfit(Data,Order=5):
import numpy as np
NewCurve = np.zeros(shape=(Data.shape[0]))
OldCurve = np.array(Data)
Diff = NewCurve-OldCurve
Convergence = np.dot(Diff,Diff)
m = 0
while Convergence > 1: # Suggest setting convergence criteria == pixel resolution
P = np.polyfit(range(len(Data)),OldCurve,Order)
NewCurve = np.polyval(P,range(len(Data)))
np.copyto(OldCurve, NewCurve, where = NewCurve < OldCurve)
m+=1
Diff = NewCurve - OldCurve
Convergence = np.dot(Diff,Diff)
#print('Iterations needed for convergence: ',m,sep='')
CurveFit=np.copy(NewCurve)
return (CurveFit)
def use(self, dataset):
"""
Computes and returns the outputs of the Learner for
``dataset``:
- the outputs should be a Numpy 2D array of size
len(dataset) by (nb of classes + 1)
- the ith row of the array contains the outputs for the ith example
- the outputs for each example should contain
the predicted class (first element) and the
output probabilities for each class (following elements)
Argument ``dataset`` is an MLProblem object.
"""
outputs = np.zeros((len(dataset), self.n_classes + 1))
errors = np.zeros((len(dataset), 2))
## PUT CODE HERE ##
# row[0] is input.csv image (array), row[1] actual target class for that image
for ind, row in enumerate(dataset):
# fill 2nd element with loss
errors[ind, 1] = self.fprop(row[0], row[1])
# predicted class
outputs[ind, 0] = np.argmax(self.hs[-1])
# 0/1 classification error
errors[ind, 0] = (outputs[ind, 0] != row[1])
# print "errors: ", errors[ind, ]
# add output probs
np.copyto(outputs[ind, 1:], self.hs[-1])
# print "outputs: ", outputs[ind,]
# time.sleep(5)
return outputs, errors
def set_rho(self, rho):
"""
Set the initial density matrix
:param rho: 2D numpy array or sting containing the density matrix
:return: self
"""
if isinstance(rho, str):
# density matrix is supplied as a string
ne.evaluate("%s + 0j" % rho, local_dict=vars(self), out=self.rho)
elif isinstance(rho, np.ndarray):
# density matrix is supplied as an array
# perform the consistency checks
assert rho.shape == self.rho.shape,\
"The grid size does not match with the density matrix"
# make sure the density matrix is stored as a complex array
np.copyto(self.rho, rho.astype(np.complex))
else:
raise ValueError("density matrix must be either string or numpy.array")
# normalize
self.rho /= self.rho.trace() * self.dX
return self
def revert_all(self, clear=False):
'''return the image to it's original state'''
np.copyto(self.image, self.ref_image)
if clear:
self.clear_history()
self.drawing.reset()
def __init__(self, W = 20, H = 20):
""" Inicializa Variables """
self.WIDTH = W
self.HEIGHT = H
self.prevState = numpy.zeros((self.HEIGHT,self.WIDTH))
self.nextState = numpy.zeros((self.HEIGHT,self.WIDTH))
numpy.copyto(self.prevState,self.nextState)
def get_shape_composition(self, LABEL_MAP, query_shapes, query_scores, exemplar_shapes):
image_outputs = {}
for i in range(0, self.TOP_K):
image_outputs[i] = {}
image_outputs[i]['shape_im'] = np.zeros(
(np.size(LABEL_MAP, 0), np.size(LABEL_MAP, 1), 3))
image_outputs[i]['part_im'] = np.zeros(
(np.size(LABEL_MAP, 0), np.size(LABEL_MAP, 1), 3))
image_outputs[i]['comp_mask'] = np.zeros(
(np.size(LABEL_MAP, 0), np.size(LABEL_MAP, 1)))
image_outputs[i]['mask'] = np.zeros(
(np.size(LABEL_MAP, 0), np.size(LABEL_MAP, 1)))
for i in range(0, len(query_shapes)):
ith_score = query_scores[i]['score']
if ith_score.size == 0:
continue
# get the box info--
ith_bbx = query_shapes[i]['bbox']
ith_org_mask = query_shapes[i]['comp_shape']
# get the parts feat --
if self.IS_PARTS == 1:
ith_part_feat = self.get_part_feat(
query_shapes[i]['comp_context'])
for l in range(0, np.size(ith_score, 0)):
lth_nn_img = np.zeros(
(np.size(LABEL_MAP, 0), np.size(LABEL_MAP, 1), 3))
lth_nn_part_img = np.zeros(
(np.size(LABEL_MAP, 0), np.size(LABEL_MAP, 1), 3))
lth_nn = exemplar_shapes[ith_score[l, 6]]
lth_nn_rgb = np.empty_like(lth_nn['comp_rgb'])
np.copyto(lth_nn_rgb, lth_nn['comp_rgb'])
lth_nn_context = np.empty_like(lth_nn['comp_context'])
np.copyto(lth_nn_context, lth_nn['comp_context'])
lth_nn_rgb = cv2.resize(lth_nn_rgb, dsize=(
np.size(ith_org_mask, 1), np.size(ith_org_mask, 0)))
lth_nn_rgb[:, :, 0] = lth_nn_rgb[:, :, 0]*ith_org_mask
lth_nn_rgb[:, :, 1] = lth_nn_rgb[:, :, 1]*ith_org_mask
lth_nn_rgb[:, :, 2] = lth_nn_rgb[:, :, 2]*ith_org_mask
lth_nn_img[ith_bbx[0]:ith_bbx[2],
ith_bbx[1]:ith_bbx[3], :] = lth_nn_rgb
if self.IS_PARTS == 1:
lth_part_feat = self.get_part_feat(lth_nn_context)
lth_part_scores = self.get_part_scores(
ith_part_feat, lth_part_feat)
lth_nn_part_img[ith_bbx[0]:ith_bbx[2], ith_bbx[1]:ith_bbx[3], :] = \
self.get_part_image(lth_nn['org_rgb'], lth_part_scores,
ith_org_mask)
image_outputs[l]['part_im'] = image_outputs[l]['part_im'] + \
lth_nn_part_img
image_outputs[l]['shape_im'] = image_outputs[l]['shape_im'] + lth_nn_img
image_outputs[l]['comp_mask'] = ~((lth_nn_img[:, :, 0] == 0) & (
lth_nn_img[:, :, 1] == 0) & (lth_nn_img[:, :, 2] == 0))
image_outputs[l]['mask'] = image_outputs[l]['mask'] + \
image_outputs[l]['comp_mask']
return image_outputs
def setStateSpace(self, _A, _B, _C, _D):
assert _A.shape == (self.n_states, self.n_states)
assert _B.shape == (self.n_states, self.n_inputs)
assert _C.shape == (self.n_outputs, self.n_states)
assert _D.shape == (self.n_outputs, self.n_inputs)
np.copyto(self.A, _A)
np.copyto(self.B, _B)
np.copyto(self.C, _C)
np.copyto(self.D, _D)
# State Transition Matrix
np.copyto(self.state_trans, expm(self.A * self.d_time))
np.copyto(self.B_dt, self.B * self.d_time)
def setInput(self, _u):
assert _u.shape == (self.n_inputs, 1)
np.copyto(self.u, _u)
def cholesky(in_arr, out_arr, n):
np.copyto(out_arr, np.linalg.cholesky(in_arr))
def construct_zernike_polynomials(x,
y,
zernike_indexes,
mask=None,
weight=None):
"""Return the zerike polynomials for all objects in an image
x - the X distance of a point from the center of its object
y - the Y distance of a point from the center of its object
zernike_indexes - an Nx2 array of the Zernike polynomials to be computed.
mask - a mask with same shape as X and Y of the points to consider
weight - weightings of points with the same shape as X and Y (default
weight on each point is 1).
returns a height x width x N array of complex numbers which are the
e^i portion of the sine and cosine of the Zernikes
"""
if x.shape != y.shape:
raise ValueError("X and Y must have the same shape")
if mask is None:
pass
elif mask.shape != x.shape:
raise ValueError("The mask must have the same shape as X and Y")
else:
x = x[mask]
y = y[mask]
if weight is not None:
weight = weight[mask]
lut = construct_zernike_lookuptable(
zernike_indexes) # precompute poly. coeffs.
nzernikes = zernike_indexes.shape[0]
# compute radii
r_square = np.square(x) # r_square = x**2
np.add(r_square, np.square(y), out=r_square) # r_square = x**2 + y**2
# z = y + 1j*x
# each Zernike polynomial is poly(r)*(r**m * np.exp(1j*m*phi)) ==
# poly(r)*(y + 1j*x)**m
z = np.empty(x.shape, np.complex)
np.copyto(z.real, y)
np.copyto(z.imag, x)
# preallocate buffers
s = np.empty_like(x)
zf = np.zeros((nzernikes, ) + x.shape, np.complex)
z_pows = {}
for idx, (n, m) in zip(range(nzernikes), zernike_indexes):
s[:] = 0
if not m in z_pows:
if m == 0:
z_pows[m] = np.complex(1.0)
else:
z_pows[m] = z if m == 1 else (z**m)
z_pow = z_pows[m]
# use Horner scheme
for k in range((n - m) / 2 + 1):
s *= r_square
s += lut[idx, k]
s[r_square > 1] = 0
if weight is not None:
s *= weight.astype(s.dtype)
if m == 0:
np.copyto(zf[idx], s) # zf[idx] = s
else:
np.multiply(s, z_pow, out=zf[idx]) # zf[idx] = s*exp_term
if mask is None:
result = zf.transpose(tuple(range(1, 1 + x.ndim)) + (0, ))
else:
result = np.zeros(mask.shape + (nzernikes, ), np.complex)
result[mask] = zf.transpose(tuple(range(1, 1 + x.ndim)) + (0, ))
return result
import numpy as np
import cv2 as cv
src = cv.imread("./image/building.JPG")
cv.namedWindow("src")
cv.imshow("src", src)
cv.moveWindow("src", 0, 0)
row, col, channel = src.shape[:]
canny = cv.Canny(src, 75, 150)
cv.imshow("canny", canny)
lines = cv.HoughLinesP(canny,
1.0,
np.pi / 180,
150,
minLineLength=20,
maxLineGap=10)
dst = np.zeros((row, col, channel), np.uint8)
np.copyto(dst, src)
for line in lines:
x1, y1, x2, y2 = line[0]
cv.line(dst, (x1, y1), (x2, y2), (0, 0, 255), 1, cv.LINE_AA)
cv.imshow("dst", dst)
print(lines)
cv.waitKey(0)
def get_query_shapes(self, LABEL_MAP, INST_MAP):
instances = INST_MAP[:, :, 0]*10 + \
INST_MAP[:, :, 1]*100 + \
INST_MAP[:, :, 2]*1000
category_list = np.unique(LABEL_MAP)
category_list = category_list[category_list != self.IGNORE_LABEL]
query_shapes = {}
iter_ = 0
#pdb.set_trace()
for n in range(0, len(category_list)):
# for each semantic label map --
nth_label_map = np.int16(LABEL_MAP)
nth_label_map[nth_label_map != category_list[n]] = -1
nth_label_map[nth_label_map == category_list[n]] = 1
nth_label_map[nth_label_map == -1] = 0
# ---------------------------------------------------
# check if this category belongs to things or stuff--
is_thing = self.label_data[self.label_data[:, 0]
== category_list[n], 4]
if(is_thing != 1):
[n_r, n_c] = np.nonzero(nth_label_map)
y1 = np.amin(n_r)
y2 = np.amax(n_r)
x1 = np.amin(n_c)
x2 = np.amax(n_c)
comp_shape = nth_label_map[y1:y2, x1:x2]
comp_context = LABEL_MAP[y1:y2, x1:x2]
ar = np.size(
comp_context, 0)/(float(np.size(comp_context, 1)) + sys.float_info.epsilon)
# -- get the comp-list
query_shapes[iter_] = {}
query_shapes[iter_]['comp_shape'] = comp_shape
query_shapes[iter_]['comp_context'] = comp_context
query_shapes[iter_]['shape_label'] = category_list[n]
query_shapes[iter_]['ar'] = ar
query_shapes[iter_]['bbox'] = [y1, x1, y2, x2]
query_shapes[iter_]['dim'] = [
np.size(nth_label_map, 0), np.size(nth_label_map, 1)]
iter_ = iter_ + 1
else:
nth_inst_map = np.empty_like(instances)
np.copyto(nth_inst_map, instances)
nth_inst_map[nth_inst_map == 0] = -1
nth_inst_map[nth_label_map == 0] = -1
nth_inst_ids = np.unique(nth_inst_map)
nth_inst_ids = nth_inst_ids[nth_inst_ids != -1]
for m in range(0, len(nth_inst_ids)):
mth_inst_map = np.empty_like(nth_inst_map)
np.copyto(mth_inst_map, nth_inst_map)
mth_inst_map[mth_inst_map != nth_inst_ids[m]] = -1
mth_inst_map[mth_inst_map == nth_inst_ids[m]] = 1
mth_inst_map[mth_inst_map == -1] = 0
# --
[m_r, m_c] = np.nonzero(mth_inst_map)
y1 = np.amin(m_r)
y2 = np.amax(m_r)
x1 = np.amin(m_c)
x2 = np.amax(m_c)
comp_shape = mth_inst_map[y1:y2, x1:x2]
comp_context = LABEL_MAP[y1:y2, x1:x2]
ar = np.size(
comp_context, 0)/(float(np.size(comp_context, 1)) + sys.float_info.epsilon)
# -- comp_list data
query_shapes[iter_] = {}
query_shapes[iter_]['comp_shape'] = comp_shape
query_shapes[iter_]['comp_context'] = comp_context
query_shapes[iter_]['shape_label'] = category_list[n]
query_shapes[iter_]['ar'] = ar
query_shapes[iter_]['bbox'] = [y1, x1, y2, x2]
query_shapes[iter_]['dim'] = [
np.size(nth_label_map, 0), np.size(nth_label_map, 1)]
iter_ = iter_ + 1
return query_shapes
def get_exemplar_shapes(self, EXEMPLAR_MATCHES):
exemplar_shapes = {}
iter_ = 0
for i in range(0, len(EXEMPLAR_MATCHES)):
LABEL_MAP = cv2.imread(self.LABEL_PATH + EXEMPLAR_MATCHES[i][0].decode('UTF-8'))
LABEL_MAP = np.int16(LABEL_MAP[:, :, 0])
INST_MAP = np.int32(cv2.imread(
self.INST_PATH + EXEMPLAR_MATCHES[i][0].decode('UTF-8')))
EXEMPLAR_IMAGE = cv2.imread(self.IMAGE_PATH +
(EXEMPLAR_MATCHES[i][0]).decode('UTF-8').replace('.png', '.jpg'))
instances = INST_MAP[:, :, 0]*10 + \
INST_MAP[:, :, 1]*100 + \
INST_MAP[:, :, 2]*1000
category_list = np.unique(LABEL_MAP)
category_list = category_list[category_list != self.IGNORE_LABEL]
for n in range(0, len(category_list)):
# for each semantic label map --
nth_label_map = np.empty_like(LABEL_MAP)
np.copyto(nth_label_map, LABEL_MAP)
nth_label_map[nth_label_map != category_list[n]] = -1
nth_label_map[nth_label_map == category_list[n]] = 1
nth_label_map[nth_label_map == -1] = 0
# ---------------------------------------------------
# check if this category belongs to things or stuff--
is_thing = self.label_data[self.label_data[:, 0]
== category_list[n], 4]
if(is_thing != 1):
[n_r, n_c] = np.nonzero(nth_label_map)
y1 = np.amin(n_r)
y2 = np.amax(n_r)
x1 = np.amin(n_c)
x2 = np.amax(n_c)
comp_shape = nth_label_map[y1:y2, x1:x2]
comp_context = LABEL_MAP[y1:y2, x1:x2]
comp_rgb = np.empty_like(EXEMPLAR_IMAGE[y1:y2, x1:x2, :])
np.copyto(comp_rgb, EXEMPLAR_IMAGE[y1:y2, x1:x2, :])
comp_rgb[:, :, 0] = comp_rgb[:, :, 0] * comp_shape
comp_rgb[:, :, 1] = comp_rgb[:, :, 1] * comp_shape
comp_rgb[:, :, 2] = comp_rgb[:, :, 2] * comp_shape
ar = np.size(
comp_context, 0)/(float(np.size(comp_context, 1)) + sys.float_info.epsilon)
# -- get the comp-list
exemplar_shapes[iter_] = {}
exemplar_shapes[iter_]['comp_shape'] = comp_shape
exemplar_shapes[iter_]['comp_context'] = comp_context
exemplar_shapes[iter_]['comp_rgb'] = comp_rgb
exemplar_shapes[iter_]['org_rgb'] = EXEMPLAR_IMAGE[y1:y2, x1:x2, :]
exemplar_shapes[iter_]['shape_label'] = category_list[n]
exemplar_shapes[iter_]['ar'] = ar
exemplar_shapes[iter_]['bbox'] = [y1, x1, y2, x2]
exemplar_shapes[iter_]['dim'] = [
np.size(nth_label_map, 0), np.size(nth_label_map, 1)]
iter_ = iter_ + 1
else:
nth_inst_map = np.empty_like(instances)
np.copyto(nth_inst_map, instances)
nth_inst_map[nth_inst_map == 0] = -1
nth_inst_map[nth_label_map == 0] = -1
nth_inst_ids = np.unique(nth_inst_map)
nth_inst_ids = nth_inst_ids[nth_inst_ids != -1]
for m in range(0, len(nth_inst_ids)):
mth_inst_map = np.empty_like(nth_inst_map)
np.copyto(mth_inst_map, nth_inst_map)
mth_inst_map[mth_inst_map != nth_inst_ids[m]] = -1
mth_inst_map[mth_inst_map == nth_inst_ids[m]] = 1
mth_inst_map[mth_inst_map == -1] = 0
# --
[m_r, m_c] = np.nonzero(mth_inst_map)
y1 = np.amin(m_r)
y2 = np.amax(m_r)
x1 = np.amin(m_c)
x2 = np.amax(m_c)
comp_shape = mth_inst_map[y1:y2, x1:x2]
comp_context = LABEL_MAP[y1:y2, x1:x2]
comp_rgb = np.empty_like(
EXEMPLAR_IMAGE[y1:y2, x1:x2, :])
np.copyto(comp_rgb, EXEMPLAR_IMAGE[y1:y2, x1:x2, :])
comp_rgb[:, :, 0] = comp_rgb[:, :, 0] * comp_shape
comp_rgb[:, :, 1] = comp_rgb[:, :, 1] * comp_shape
comp_rgb[:, :, 2] = comp_rgb[:, :, 2] * comp_shape
ar = np.size(
comp_context, 0)/(float(np.size(comp_context, 1)) + sys.float_info.epsilon)
# -- comp_list data
exemplar_shapes[iter_] = {}
exemplar_shapes[iter_]['comp_shape'] = comp_shape
exemplar_shapes[iter_]['comp_context'] = comp_context
exemplar_shapes[iter_]['comp_rgb'] = comp_rgb
exemplar_shapes[iter_]['org_rgb'] = EXEMPLAR_IMAGE[y1:y2, x1:x2, :]
exemplar_shapes[iter_]['shape_label'] = category_list[n]
exemplar_shapes[iter_]['ar'] = ar
exemplar_shapes[iter_]['bbox'] = [y1, x1, y2, x2]
exemplar_shapes[iter_]['dim'] = [
np.size(nth_label_map, 0), np.size(nth_label_map, 1)]
iter_ = iter_ + 1
return exemplar_shapes
def loadParameters(self, pD):
try:
np.copyto(self.mean, pD[self.instanceName + ':mean'])
np.copyto(self.var, pD[self.instanceName + ':std'])
except:
self.printError("Layer parameter(s) does not exist while loading.")
def get_matching_score(self, query_data, exemplar_data):
# query data --
query_shape = np.empty_like(query_data['comp_shape'])
np.copyto(query_shape, query_data['comp_shape'])
q_h = np.size(query_shape, 0)
q_w = np.size(query_shape, 1)
if(q_h <= 1 | q_w <= 1):
return np.array([])
query_shape_rs = cv2.resize(query_shape, dsize=(self.WIN_SIZE, self.WIN_SIZE),
interpolation=cv2.INTER_NEAREST)
query_shape_rs[query_shape_rs == 0] = -1
query_label = query_data['shape_label']
query_ar = query_data['ar']
# get the relevant labels from the exemplar data
synth_data = np.zeros((len(exemplar_data), 8), dtype='float')
for j in range(0, len(exemplar_data)):
jth_label = exemplar_data[j]['shape_label']
if(jth_label != query_label):
continue
jth_shape = np.empty_like(exemplar_data[j]['comp_shape'])
np.copyto(jth_shape, exemplar_data[j]['comp_shape'])
jth_h = np.size(jth_shape, 0)
jth_w = np.size(jth_shape, 1)
if((jth_h < (self.RES_F * q_h)) | (jth_w < (self.RES_F * q_w))):
continue
jth_ar = exemplar_data[j]['ar']
ar12 = np.divide(query_ar, float(jth_ar) + sys.float_info.epsilon)
if((ar12 < 0.5) | (ar12 > 2.0)):
continue
jth_search_shape = cv2.resize(jth_shape, dsize=(self.WIN_SIZE, self.WIN_SIZE),
interpolation=cv2.INTER_NEAREST)
jth_search_shape[jth_search_shape == 0] = -1
jth_score = np.divide((query_shape_rs.flatten() * jth_search_shape.flatten()).sum(),
float(np.size(query_shape_rs, 0)*np.size(query_shape_rs, 1)) + sys.float_info.epsilon)
synth_data[j, :] = [1, 1, np.size(query_shape_rs, 1), np.size(query_shape_rs, 0),
jth_score, 0, j, 1]
synth_data = synth_data[synth_data[:, 7] == 1, :]
if synth_data.size == 0:
return synth_data
# find the exmples better than SHAPE_THRESH
val_examples = synth_data[:, 4] >= self.SHAPE_THRESH
if(val_examples.sum() == 0):
Is = np.argmax(synth_data[:, 4])
score = np.tile(synth_data[Is, :], [self.TOP_K, 1])
return score
# if there are more examples
score = synth_data[val_examples, :]
Is = np.argsort(score[:, 4])
rev_Is = Is[::-1]
score = score[rev_Is, :]
num_ex = np.minimum(np.size(score, 0), self.TOP_K)
score = score[0:num_ex, :]
if(np.size(score, 0) < self.TOP_K):
score = np.tile(score, [self.TOP_K, 1])
score = score[0:self.TOP_K, :]
return score
def runTests():
global board
initializeGame()
# This board is an extreme example of COMPLETE enclosure
print("test enclosure detection positive")
board = np.array([[0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0],
[1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1],
[1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0],
[0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1],
[0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0],
[0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 1, 0, 3, 1],
[0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0]])
if (not isVictory()):
exit('FAIL')
initializeGame()
# This crazy board is an extreme example of an INCOMPLETE enclosure
print("test enclosure detection negative")
board = np.array([[0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1],
[1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0],
[0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1],
[0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0],
[0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 1, 0, 3, 1],
[0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0]])
if (isVictory()):
exit('FAIL')
### FORCE A TEST OF REPEAT
global repeatCheckOn
repeatCheckOn = True
board = np.array([[0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 2, 0, 0, 0, 1, 0, 0, 1, 3, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 2, 0, 2, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]])
#Precedence tests
smartMove()
#Capture test
print("Capture up")
movePiece([3, 1], [2, 1])
if (board[1, 1] != 0):
exit("FAIL")
print("Simultaneous capture left, right, and down")
movePiece([5, 5], [8, 5])
if ((board[8, 4] != 0) or (board[8, 6] != 0) or (board[9, 5] != 0)):
print(board)
exit("FAIL")
print("Capture against restricted square")
movePiece([1, 5], [3, 5])
if (board[4, 5] != 0):
exit("FAIL")
print("Capture king")
movePiece([3, 9], [2, 9])
if (not gameOver):
exit("FAIL")
initializeGame()
board = np.zeros([11, 11], dtype=int) # use this to set up an empty board
board[5, 0] = 3 # set up a test board with only a king
print("Move king to restricted square (this is allowed)")
if (not movePiece([5, 0], [5, 5])):
exit("FAIL")
np.copyto(board, startingBoard)
storeState()
print("Throw error when moving attacker to restricted square")
if (movePiece([10, 7], [10, 10]) or lastReturnCode != RESTRICTED_SQUARE):
exit("FAIL")
print("Throw error when moving through a block")
if (movePiece([10, 7], [10, 5]) or lastReturnCode != WAY_BLOCKED):
exit("FAIL")
print("Throw error when starting position is empty")
if (movePiece([10, 10], [10, 5]) or lastReturnCode != NO_PIECE_TO_MOVE):
exit("FAIL")
print("Throw error when start and end positions are the same")
if (movePiece([10, 7], [10, 7]) or lastReturnCode != PIECE_DID_NOT_MOVE):
exit("FAIL")
movePiece([10, 7], [10, 8])
movePiece([10, 8], [10, 7])
movePiece([10, 7], [10, 8])
if (repeatCheckOn):
print("Throw error when board state has repeated too many times")
if (movePiece([10, 8], [10, 7]) or lastReturnCode != TOO_MANY_REPEATS):
exit("FAIL")
print("Throw error if piece moves diagonally")
if (movePiece([10, 8], [9, 5]) or lastReturnCode != NO_DIAGONALS):
exit("FAIL")
print("Throw error if piece moves off the board")
if (movePiece([10, 8], [11, 8]) or lastReturnCode != WAY_BLOCKED):
exit("FAIL")
exit("All tests PASS")
ab['var1'] = lib['ids1'] ab['var2'] = lib['strands1'] ab['var3'] = c1 ab['var4'] = lib['cuts1'] ab['var5'] = lib['rfragIdxs1'] ab['var6'] = lib['strands2'] ab['var7'] = c2 ab['var8'] = lib['cuts2'] ab['var9'] = lib['rfragIdxs2'] ab['var10'] = mapq ab['var11'] = mapq # Swap so that lowest chr numbers are to the left: np.copyto(ab['var3'], c2, where=swap) np.copyto(ab['var7'], c1, where=swap) np.copyto(ab['var2'], lib['strands2'], where=swap) np.copyto(ab['var6'], lib['strands1'], where=swap) np.copyto(ab['var4'], lib['cuts2'], where=swap) np.copyto(ab['var8'], lib['cuts1'], where=swap) np.copyto(ab['var5'], lib['rfragIdxs2'], where=swap) np.copyto(ab['var9'], lib['rfragIdxs1'], where=swap) # sort on chromosomes: #np.argsort(ab, order=['var3', 'var7']) np.savetxt(sys.stdout, ab[filter], fmt="%s %i %s %i %i %i %s %i %i %i %i")
def _write_obs(maybe_dict_obs):
flatdict = obs_to_dict(maybe_dict_obs)
for k in keys:
dst = obs_bufs[k].get_obj()
dst_np = np.frombuffer(dst, dtype=obs_dtypes[k]).reshape(obs_shapes[k]) # pylint: disable=W0212
np.copyto(dst_np, flatdict[k])
def _create_group(self, drawing, projection, viewport, mesh):
faces = mesh.faces
shader = mesh.shader or (lambda face_index, winding: {})
default_style = mesh.style or {}
# Extend each point to a vec4, then transform to clip space.
faces = np.dstack([faces, np.ones(faces.shape[:2])])
faces = np.dot(faces, projection)
# Reject trivially clipped polygons.
xyz, w = faces[:, :, :3], faces[:, :, 3:]
accepted = np.logical_and(np.greater(xyz, -w), np.less(xyz, +w))
accepted = np.all(accepted,
2) # vert is accepted if xyz are all inside
accepted = np.any(accepted,
1) # face is accepted if any vert is inside
degenerate = np.less_equal(w, 0)[:, :, 0] # vert is bad if its w <= 0
degenerate = np.any(degenerate,
1) # face is bad if any of its verts are bad
accepted = np.logical_and(accepted, np.logical_not(degenerate))
faces = np.compress(accepted, faces, axis=0)
# Apply perspective transformation.
xyz, w = faces[:, :, :3], faces[:, :, 3:]
faces = xyz / w
# Sort faces from back to front.
face_indices = self._sort_back_to_front(faces)
faces = faces[face_indices]
# Apply viewport transform to X and Y.
faces[:, :, 0:1] = (1.0 + faces[:, :, 0:1]) * viewport.width / 2
faces[:, :, 1:2] = (1.0 - faces[:, :, 1:2]) * viewport.height / 2
faces[:, :, 0:1] += viewport.minx
faces[:, :, 1:2] += viewport.miny
# Compute the winding direction of each polygon.
windings = np.zeros(faces.shape[0])
if faces.shape[1] >= 3:
p0, p1, p2 = faces[:, 0, :], faces[:, 1, :], faces[:, 2, :]
normals = np.cross(p2 - p0, p1 - p0)
np.copyto(windings, normals[:, 2])
group = drawing.g(**default_style)
# Create circles.
if mesh.circle_radius > 0:
for face_index, face in enumerate(faces):
style = shader(face_indices[face_index], 0)
if style is None:
continue
face = np.around(face[:, :2], self.precision)
for pt in face:
group.add(drawing.circle(pt, mesh.circle_radius, **style))
return group
# Create polygons and lines.
for face_index, face in enumerate(faces):
style = shader(face_indices[face_index], windings[face_index])
if style is None:
continue
face = np.around(face[:, :2], self.precision)
if len(face) == 2:
group.add(drawing.line(face[0], face[1], **style))
else:
group.add(drawing.polygon(face, **style))
return group
def prepareInputsForFpga(self,
inputs,
cfgFile,
scale,
PE=-1,
layerName=""):
startTime = timeit.default_timer()
execData = self.getOrCreateExecData(PE)
numBatches, imgSize = inputs.shape
#print "imgSize: %s, numBatch: %s" % (imgSize, numBatches)
inputPtrsNeedInit = (type(execData._cpuInputs) == type(None) \
or execData._cpuInputs.shape[0] != numBatches \
or type(execData._fpgaInputs) == type(None) \
or len(execData._fpgaInputs) != numBatches \
or execData._imgSize != imgSize)
if inputPtrsNeedInit:
# prepare src float array
execData._imgSize = imgSize
execData._cpuInputs = np.ascontiguousarray(\
np.zeros(numBatches*imgSize).reshape(numBatches, imgSize),
dtype=np.float32)
# prepare array of ptrs to each input image
execData._cpuInputPtrs = (POINTER(c_float) * numBatches)()
for i in range(numBatches):
execData._cpuInputPtrs[i] \
= execData._cpuInputs[i].ctypes.data_as(POINTER(c_float))
# prepare tgt short array
execData._fpgaInputPtrs = (POINTER(c_short) * numBatches)()
execData._fpgaInputPtrsOrig = execData._fpgaInputPtrs
# free existing mem (if any)
if execData._fpgaInputHandles:
for fps in execData._fpgaInputHandles:
for hi, h in enumerate(self._handles):
self._lib.xFree(h, fps[hi], True)
execData._fpgaInputs = []
execData._fpgaInputHandles = []
# make new mem
for i in range(numBatches):
(fpgaArr, fpgaHandles) = self.makeFPGAShortArray(imgSize)
execData._fpgaInputs.append(fpgaArr)
execData._fpgaInputPtrs[i] \
= execData._fpgaInputs[i].ctypes.data_as(POINTER(c_short))
execData._fpgaInputHandles.append(fpgaHandles)
else:
execData._fpgaInputPtrs = execData._fpgaInputPtrsOrig
if inputs.dtype == np.float32:
# grab new input data
for i in range(numBatches):
np.copyto(execData._cpuInputs[i], inputs[i])
# prepare data for FPGA
actualNumFpgaInputs = self._lib.XDNNPrepareInput(\
layerName, execData._cpuInputPtrs,
execData._fpgaInputPtrs,
numBatches, imgSize, cfgFile,
scale)
if actualNumFpgaInputs < len(execData._fpgaInputPtrs):
# quantized/packed
truncatedArr = (POINTER(c_short) * actualNumFpgaInputs)()
for i in range(actualNumFpgaInputs):
truncatedArr[i] = execData._fpgaInputPtrs[i]
execData._fpgaInputPtrs = truncatedArr
else:
# already prepared, just populate fields
for i in range(numBatches):
np.copyto(execData._fpgaInputs[i], inputs[i])
# tell FPGA there's new data
self.markDirty(execData._peMask)
elapsedTime = timeit.default_timer() - startTime
#print "PrepareInputsForFpga elapsed (%f ms):" % (elapsedTime * 1000)
return execData._fpgaInputPtrs
Angry = [img1, img2, img3]
Disgusting = [img4, img5, img6]
Fearful = [img7, img8, img9]
Happy = [img10, img11, img12]
'''Sad = [img13, img14, img15]
Surprising = [img16, img17, img18]
Neutral = [img19, img20, img21]'''
# 이미지 지정
background = Happy[x]
'''cv2.resize(원본, dsize=(0, 0),가로배수,세로배수, interpolation=cv2.INTER_LINEAR)'''
logo = cv2.imread("C:/Users/user/PycharmProjects/OpenCV/Black Layer.png")
gray_logo = cv2.cvtColor(logo, cv2.COLOR_BGR2GRAY)
_, mask_inv = cv2.threshold(gray_logo, 10, 255, cv2.THRESH_BINARY_INV)
background_height, background_width, _ = background.shape # 900, 600, 3
logo_height, logo_width, _ = logo.shape # 360, 313, 3
x = background_height - logo_height
y = (background_width - logo_width) // 2
roi = background[x:x + logo_height, y:y + logo_width]
roi_logo = cv2.add(logo, roi, mask=mask_inv)
result = cv2.add(roi_logo, logo)
np.copyto(roi, result)
cv2.imshow("result_background", background)
cv2.waitKey()
def test_copyto():
a = np.arange(6, dtype='i4').reshape(2, 3)
# Simple copy
np.copyto(a, [[3, 1, 5], [6, 2, 1]])
assert_equal(a, [[3, 1, 5], [6, 2, 1]])
# Overlapping copy should work
np.copyto(a[:, :2], a[::-1, 1::-1])
assert_equal(a, [[2, 6, 5], [1, 3, 1]])
# Defaults to 'same_kind' casting
assert_raises(TypeError, np.copyto, a, 1.5)
# Force a copy with 'unsafe' casting, truncating 1.5 to 1
np.copyto(a, 1.5, casting='unsafe')
assert_equal(a, 1)
# Copying with a mask
np.copyto(a, 3, where=[True, False, True])
assert_equal(a, [[3, 1, 3], [3, 1, 3]])
# Casting rule still applies with a mask
assert_raises(TypeError, np.copyto, a, 3.5, where=[True, False, True])
# Lists of integer 0's and 1's is ok too
np.copyto(a, 4.0, casting='unsafe', where=[[0, 1, 1], [1, 0, 0]])
assert_equal(a, [[3, 4, 4], [4, 1, 3]])
# Overlapping copy with mask should work
np.copyto(a[:, :2], a[::-1, 1::-1], where=[[0, 1], [1, 1]])
assert_equal(a, [[3, 4, 4], [4, 3, 3]])
# 'dst' must be an array
assert_raises(TypeError, np.copyto, [1, 2, 3], [2, 3, 4])
data = np.reshape(codebook[code], weights.shape)
np.copyto(weights, data)
nz_num = np.fromfile(fin, dtype=np.uint32, count=len(layers))
for idx, layer in enumerate(layers):
print "Reconstruct layer", layer
print "Total Non-zero number:", nz_num[idx]
if 'conv' in layer:
bits = 8
else:
bits = 4
codebook_size = 2**bits
codebook = np.fromfile(fin, dtype=np.float32, count=codebook_size)
bias = np.fromfile(fin,
dtype=np.float32,
count=net.params[layer][1].data.size)
np.copyto(net.params[layer][1].data, bias)
spm_stream = np.fromfile(fin,
dtype=np.uint8,
count=(nz_num[idx] - 1) / (8 / bits) + 1)
ind_stream = np.fromfile(fin,
dtype=np.uint8,
count=(nz_num[idx] - 1) / 2 + 1)
binary_to_net(net.params[layer][0].data, spm_stream, ind_stream, codebook,
nz_num[idx])
net.save(target)
def copyto(
dst: ndpoly,
src: PolyLike,
casting: str = "same_kind",
where: numpy.typing.ArrayLike = True,
) -> None:
"""
Copy values from one array to another, broadcasting as necessary.
Raises a TypeError if the `casting` rule is violated, and if
`where` is provided, it selects which elements to copy.
Args:
dst:
The array into which values are copied.
src:
The array from which values are copied.
casting:
Controls what kind of data casting may occur when copying.
* 'no' means the data types should not be cast at all.
* 'equiv' means only byte-order changes are allowed.
* 'safe' means only casts which can preserve values are allowed.
* 'same_kind' means only safe casts or casts within a kind,
like float64 to float32, are allowed.
* 'unsafe' means any data conversions may be done.
where:
A boolean array which is broadcasted to match the dimensions
of `dst`, and selects elements to copy from `src` to `dst`
wherever it contains the value True.
Examples:
>>> q0 = numpoly.variable()
>>> poly1 = numpoly.polynomial([1, q0**2, q0])
>>> poly2 = numpoly.polynomial([2, q0, 3])
>>> numpoly.copyto(poly1, poly2, where=[True, False, True])
>>> poly1
polynomial([2, q0**2, 3])
>>> numpoly.copyto(poly1, poly2)
>>> poly1
polynomial([2, q0, 3])
"""
logger = logging.getLogger(__name__)
src = numpoly.aspolynomial(src)
assert isinstance(dst, numpy.ndarray)
if not isinstance(dst, numpoly.ndpoly):
if dst.dtype.names is None:
if src.isconstant():
return numpy.copyto(dst,
src.tonumpy(),
casting=casting,
where=where)
raise ValueError(f"Could not convert src {src} to dst {dst}")
if casting != "unsafe":
raise ValueError(
f"could not safely convert src {src} to dst {dst}")
logger.warning("Copying ndpoly input into ndarray")
logger.warning("You might need to cast `numpoly.polynomial(dst)`.")
logger.warning("Indeterminant names might be lost.")
dst_keys = dst.dtype.names
dst_ = dst
else:
dst_keys = dst.keys
src, _ = numpoly.align_indeterminants(src, dst)
dst_ = dst.values
missing_keys = set(src.keys).difference(dst_keys)
if missing_keys:
raise ValueError(f"memory layouts are incompatible: {missing_keys}")
for key in dst_keys:
if key in src.keys:
numpy.copyto(dst_[key],
src.values[key],
casting=casting,
where=where)
else:
numpy.copyto(dst_[key],
numpy.array(False, dtype=dst_[key].dtype),
casting=casting,
where=where)
def test_copyto_permut():
# test explicit overflow case
pad = 500
l = [True] * pad + [True, True, True, True]
r = np.zeros(len(l)-pad)
d = np.ones(len(l)-pad)
mask = np.array(l)[pad:]
np.copyto(r, d, where=mask[::-1])
# test all permutation of possible masks, 9 should be sufficient for
# current 4 byte unrolled code
power = 9
d = np.ones(power)
for i in range(2**power):
r = np.zeros(power)
l = [(i & x) != 0 for x in range(power)]
mask = np.array(l)
np.copyto(r, d, where=mask)
assert_array_equal(r == 1, l)
assert_equal(r.sum(), sum(l))
r = np.zeros(power)
np.copyto(r, d, where=mask[::-1])
assert_array_equal(r == 1, l[::-1])
assert_equal(r.sum(), sum(l))
r = np.zeros(power)
np.copyto(r[::2], d[::2], where=mask[::2])
assert_array_equal(r[::2] == 1, l[::2])
assert_equal(r[::2].sum(), sum(l[::2]))
r = np.zeros(power)
np.copyto(r[::2], d[::2], where=mask[::-2])
assert_array_equal(r[::2] == 1, l[::-2])
assert_equal(r[::2].sum(), sum(l[::-2]))
for c in [0xFF, 0x7F, 0x02, 0x10]:
r = np.zeros(power)
mask = np.array(l)
imask = np.array(l).view(np.uint8)
imask[mask != 0] = c
np.copyto(r, d, where=mask)
assert_array_equal(r == 1, l)
assert_equal(r.sum(), sum(l))
r = np.zeros(power)
np.copyto(r, d, where=True)
assert_equal(r.sum(), r.size)
r = np.ones(power)
d = np.zeros(power)
np.copyto(r, d, where=False)
assert_equal(r.sum(), r.size)
def add_expert(self, obs, action, reward, next_obs, done, done_no_max):
for a in range(4):
self.k += 1
np.copyto(self.obses[self.idx], obs)
np.copyto(self.actions[self.idx], a)
np.copyto(self.rewards[self.idx], reward)
np.copyto(self.next_obses[self.idx], next_obs)
np.copyto(self.not_dones[self.idx], not done)
np.copyto(self.not_dones_no_max[self.idx], not done_no_max)
self.idx = (self.idx + 1) % self.capacity
self.full = self.full or self.idx == 0
def save(self, ndarray: np.ndarray) -> None:
assert isinstance(ndarray, np.ndarray)
dst = self.arr.get_obj()
dst_np = np.frombuffer(dst, dtype=self.dtype).reshape(self.shape)
np.copyto(dst_np, ndarray)
def setStateSpace(self, _A_hat, _K, _H, _C, _D):
assert _A_hat.shape == (self.n_states, self.n_states)
assert _K.shape == (self.n_states, self.n_outputs)
assert _H.shape == (self.n_states, self.n_inputs)
assert _C.shape == (self.n_outputs, self.n_states)
assert _D.shape == (self.n_outputs, self.n_inputs)
np.copyto(self.A_hat, _A_hat)
np.copyto(self.K, _K)
np.copyto(self.H, _H)
np.copyto(self.C, _C)
np.copyto(self.D, _D)
# State Transition Matrix
np.copyto(self.state_trans, expm(self.A_hat * self.d_time))
np.copyto(self.st_I_dt,
(np.eye(self.n_states) + self.state_trans) * self.d_time)
def load_data(self, data):
np.copyto(self.data, data)
def config_table(maxf, n, sort=True, fun=None, index_range=None, minf=None):
"""
Tabulate a list of configurations in `n` indices
with fun(config) <= `maxf`.
Parameters
----------
maxf : scalar
The maximum excitation.
n : int
The number of indices.
sort : boolean
If True (default), the configuration table is sorted
by the excitation function.
fun : function, optional
The excitation function. If None (default), the sum
of indices is used.
index_range : array_like
The dimension of each index. The default is infinite.
minf : scalar
The minimum excitation. The default is negative infinity.
Allowed configurations are *strictly greater than* `minf`.
Returns
-------
ndarray
Notes
-----
Custom exciations functions `fun` take an argument
''configs'' with is a (..., `n`) array_like containing
an array of configuration index vectors. It must return
a (...)-shaped array with the excitation value.
The excitation value must be monotonically increasing with
an increment of one or more configuration indices.
"""
if fun is None:
fun = lambda x: np.sum(x, axis=-1) # sum over last axis
if index_range is None:
index_range = [np.inf for i in range(n)] # Maximum index values
if minf is None:
minf = -np.inf # Negative infinity
#######################################################
# Loop once to count how many configurations there are.
# Then repeat and actually record them.
#
nconfig = 0
for record in [False, True]:
if record:
table = np.zeros((nconfig, n), dtype=np.int32)
config = np.zeros((n, ), dtype=np.uint32)
nconfig = 0
if fun(config) > maxf: # The [0 0 0 ... ] configuration is already
# greater than the maximum excitation. Return an empty table.
return np.zeros((0, n))
# Otherwise, continue
if fun(config) > minf: # Strictly greater than minf !
nconfig += 1 # Count [0 0 0 0 ...]
if record:
np.copyto(table[0, :], config)
while True:
#
# Attempt to increment the current configuration `config`
#
#
next_config = config.copy()
found_valid = False
for j in range(n):
# Try to increment index j, starting from the left.
next_config[j] += 1
if next_config[j] < index_range[j] and fun(
next_config) <= maxf:
# This config is okay.
if fun(next_config) > minf: # Strictly greater than minf!
nconfig += 1
config = next_config
found_valid = True
break
else: # next_config is out-of-bounds
# Set this index to zero and move on to the next one
next_config[j] = 0
if not found_valid: # We were not able to find a new valid configuration
break
elif found_valid and record and fun(next_config) > minf:
np.copyto(table[nconfig - 1, :], next_config)
if sort:
# First sort table by configuration indices
for j in range(n - 1, -1, -1):
I = np.argsort(-table[:, j], kind='stable')
table = table[I, :] # descending sort
# Then sort by the excitation number of each configuration
# This will leave the final table sorted by excitation first,
# and then by each column left-to-right
I = np.argsort(fun(table), kind='stable')
table = table[I, :] # Sort table by the excitation number
return table
def backward_pass(self, activate_second_order_dynamics=0):
################## defining local functions & variables for faster access ################
partials_list = self.partials_list
k = np.copy(self.k)
K = np.copy(self.K)
V_x = np.copy(self.V_x)
V_xx = np.copy(self.V_xx)
##########################################################################################
V_x[self.N - 1] = self.l_x_f(self.X_p[self.N - 1])
np.copyto(V_xx[self.N - 1], 2 * self.Q_final)
#initialize before forward pass
del_J_alpha = 0
for t in range(self.N - 1, -1, -1):
if t > 0:
Q_x, Q_u, Q_xx, Q_uu, Q_ux = partials_list(
self.X_p[t - 1], self.U_p[t], V_x[t], V_xx[t],
activate_second_order_dynamics)
elif t == 0:
Q_x, Q_u, Q_xx, Q_uu, Q_ux = partials_list(
self.X_p_0, self.U_p[0], V_x[0], V_xx[0],
activate_second_order_dynamics)
try:
# If a matrix cannot be positive-definite, that means it cannot be cholesky decomposed
np.linalg.cholesky(Q_uu)
except np.linalg.LinAlgError:
print("FAILED! Q_uu is not Positive definite at t=", t)
b_pass_success_flag = 0
# If Q_uu is not positive definite, revert to the earlier values
np.copyto(k, self.k)
np.copyto(K, self.K)
np.copyto(V_x, self.V_x)
np.copyto(V_xx, self.V_xx)
break
else:
b_pass_success_flag = 1
# control-limited as follows
k[t] = -(np.linalg.inv(Q_uu) @ Q_u)
K[t] = -(np.linalg.inv(Q_uu) @ Q_ux)
del_J_alpha += -self.alpha * (
(k[t].T) @ Q_u) - 0.5 * self.alpha**2 * (
(k[t].T) @ (Q_uu @ k[t]))
if t > 0:
V_x[t - 1] = Q_x + (K[t].T) @ (Q_uu @ k[t]) + (
(K[t].T) @ Q_u) + ((Q_ux.T) @ k[t])
V_xx[t - 1] = Q_xx + ((K[t].T) @ (Q_uu @ K[t])) + (
(K[t].T) @ Q_ux) + ((Q_ux.T) @ K[t])
######################### Update the new gains ##############################################
np.copyto(self.k, k)
np.copyto(self.K, K)
np.copyto(self.V_x, V_x)
np.copyto(self.V_xx, V_xx)
#############################################################################################
self.count += 1
return b_pass_success_flag, del_J_alpha
def values(self, corr_plus, corr_cross, snrv, psd, indices, template_plus,
template_cross, u_vals, hplus_cross_corr, hpnorm, hcnorm):
""" Calculate the chisq at points given by indices.
Returns
-------
chisq: Array
Chisq values, one for each sample index
chisq_dof: Array
Number of statistical degrees of freedom for the chisq test
in the given template
"""
if self.do:
num_above = len(indices)
if self.snr_threshold:
above = abs(snrv) > self.snr_threshold
num_above = above.sum()
logging.info('%s above chisq activation threshold' % num_above)
above_indices = indices[above]
above_snrv = snrv[above]
u_vals = u_vals[above]
rchisq = numpy.zeros(len(indices), dtype=numpy.float32)
dof = -100
else:
above_indices = indices
above_snrv = snrv
if num_above > 0:
chisq = []
curr_tmplt_mult_fac = 0.
curr_corr_mult_fac = 0.
if self.template_mem is None or \
(not len(self.template_mem) == len(template_plus)):
self.template_mem = zeros(
len(template_plus),
dtype=complex_same_precision_as(corr_plus))
if self.corr_mem is None or \
(not len(self.corr_mem) == len(corr_plus)):
self.corr_mem = zeros(
len(corr_plus),
dtype=complex_same_precision_as(corr_plus))
tmplt_data = template_cross.data
corr_data = corr_cross.data
numpy.copyto(self.template_mem.data, template_cross.data)
numpy.copyto(self.corr_mem.data, corr_cross.data)
template_cross._data = self.template_mem.data
corr_cross._data = self.corr_mem.data
for lidx, index in enumerate(above_indices):
above_local_indices = numpy.array([index])
above_local_snr = numpy.array([above_snrv[lidx]])
local_u_val = u_vals[lidx]
# Construct template from _plus and _cross
# Note that this modifies in place, so we store that and
# revert on the next pass.
template = template_cross.multiply_and_add(
template_plus, local_u_val - curr_tmplt_mult_fac)
curr_tmplt_mult_fac = local_u_val
template.f_lower = template_plus.f_lower
template.params = template_plus.params
# Construct the corr vector
norm_fac = local_u_val * local_u_val + 1
norm_fac += 2 * local_u_val * hplus_cross_corr
norm_fac = hcnorm / (norm_fac**0.5)
hp_fac = local_u_val * hpnorm / hcnorm
corr = corr_cross.multiply_and_add(
corr_plus, hp_fac - curr_corr_mult_fac)
curr_corr_mult_fac = hp_fac
bins = self.calculate_chisq_bins(template, psd)
dof = (len(bins) - 1) * 2 - 2
curr_chisq = power_chisq_at_points_from_precomputed(
corr, above_local_snr / norm_fac, norm_fac, bins,
above_local_indices)
chisq.append(curr_chisq[0])
chisq = numpy.array(chisq)
# Must reset corr and template to original values!
template_cross._data = tmplt_data
corr_cross._data = corr_data
if self.snr_threshold:
if num_above > 0:
rchisq[above] = chisq
else:
rchisq = chisq
return rchisq, numpy.repeat(
dof, len(indices)) # dof * numpy.ones_like(indices)
else:
return None, None
"nullval": nullval,
"t_Q": t_Q,
"p_up": p.copy(),
"v": np.zeros([nchar]),
"tmp": np.zeros([nchar + 1]),
"motherRow": np.zeros([nchar + 1]),
"childlist": childlist
}
var["nodelist-up"] = var["nodelist"].copy()
tip_states = None
pi = "Fitzjohn"
np.copyto(var["nodelist"], var["nodelistOrig"])
var["root_priors"].fill(1.0)
import copy
var1 = copy.deepcopy(var)
preallocated_arrays = var
cProfile.run("hrm_multipass(tree, chars, Q, 2, preallocated_arrays=var)")
Qtype = "Simple"
Qparams = np.array([0.0, 1.0, 0.00, 1.0, 1.0, 5.5, 1.0, 5.5])
pi = "Fitzjohn"
l_single = discrete.create_likelihood_function_hrm_mk(tree=tree,
chars=chars,
def run_game(self,
epsilon,
state,
run_type=0,
pareto_filter=None,
default_action=-1):
""""Run a Game"""
# reset game for the next epoch
gamma = 0.1 # since immediate rewards are more important keep gamma low
steps = 0
action_def = 0
action_att = 0
reward_sum = 0
vector_reward_sum = np.zeros(3, dtype=np.int)
scalar_att = state.config.scalarize_att
att_mag = np.sqrt(np.einsum('i,i', scalar_att, scalar_att))
nn_input_old = np.zeros(state.size_graph + 2,
dtype=np.int) # +2 for the game points
# run_type == 0 (RANDOM) means random defender actions
if run_type == 0:
epsilon = 1
# log variables
self.log_object.chosen_action = ""
check_one = np.zeros(3, dtype=float)
check_two = np.zeros(3, dtype=float)
check_three = np.zeros(3, dtype=float)
# make sure that there is a reward matrix
if isinstance(state, State) and not isinstance(state, ChaosState):
state.reset_reward_matrix()
# run the game
while state.get_points(True) > 0 and state.get_points(
False) > 0 and steps < 200:
# find the Q-values for the state-action pairs
q_table = self.model.predict(state.nn_input.reshape(
1, state.nn_input.size),
batch_size=1)
# guess an attack action
# determine the valid attack actions
num_moves = state.size_graph + 1
indices = np.zeros(num_moves, dtype=np.int)
for i in range(num_moves):
indices[i] = i
indices = indices[state.actions_att]
# assume a random defense action
assumed_def = state.size_graph
for j in range(100):
assumed_def = np.random.randint(0, state.size_graph)
if state.actions_pareto_def[assumed_def] == 1:
break
# determine the attacker reward for the assumed defense action
rew_att = np.zeros(num_moves * 3, np.int).reshape(num_moves, 3)
if isinstance(state, State):
if isinstance(state, ChaosState):
np.copyto(
rew_att,
state.reward_matrix[assumed_def *
num_moves:(assumed_def + 1) *
num_moves])
else:
np.copyto(rew_att, state.reward_matrix[assumed_def, :, :])
# calculate the cosine of the angle between the attacker scalarization and the rewards
cosine = np.zeros(len(indices), dtype=float)
for i in range(len(indices)):
cosine[i] = np.absolute(
np.dot(scalar_att, rew_att[indices[i]]) /
(att_mag * np.sqrt(
np.einsum('i,i', rew_att[indices[i]],
rew_att[indices[i]]))))
# choose the strategy closest to the attacker scalarization
action_att = indices[np.argmax(cosine)]
# determine the valid defense actions based on the model and algorithm
if isinstance(state, ChaosState):
# determine the valid moves by using the chaos state method
state.scalarized_attack_actions(action_att, run_type)
else:
if run_type == 2:
# use the precalculated pareto front for the first move
if steps == 0 and pareto_filter is not None:
state.actions_pareto_def = pareto_filter
else:
state.pareto_defense_actions()
else:
# use entire set
state.actions_pareto_def = state.actions_def
# choose an actual attack action
if np.random.rand() < (0.45 if run_type == 4 else 0.3):
action_att = indices[np.random.randint(0, len(indices))]
# choose a defense action
if default_action >= 0:
# default action assigned means we just want to run a single action once
action_def = default_action
steps = 200
elif np.random.rand() < epsilon:
# random action
for j in range(100):
action_def = np.random.randint(0, state.size_graph)
if state.actions_pareto_def[action_def] == 1:
break
else:
# from Q(s,a) values
action_def = np.argmax(
np.multiply(state.actions_pareto_def,
q_table[0] - min(q_table[0])))
# Take actions, observe new state
np.copyto(nn_input_old, state.nn_input)
state.make_move(action_att, action_def)
# Update the score
score_now = state.get_score(True)
score_old = state.get_score(False)
score = np.subtract(score_now, [0, 0, score_old[2]])
vector_reward_sum += score
np.copyto(check_one, score)
# calculate the reward
# goal_scalarization = np.array([5, 5, 0], dtype=np.int)
score = np.subtract(score, self.offset)
np.copyto(check_two, score)
score = np.divide(score * 100, self.normalizer)
reward = np.dot(state.config.scalarization, score) / np.sum(
state.config.scalarization)
# reward = np.dot(goal_scalarization, score) / np.sum(goal_scalarization)
np.copyto(check_three, score)
# Get max_Q(S',a)
if run_type > 0:
q_table_new_state = self.model.predict(state.nn_input.reshape(
1, state.nn_input.size),
batch_size=1)
maxQ = np.max(q_table_new_state)
# update the q_table
update = (reward + (gamma * maxQ))
q_table[0][action_def] = update
self.model.fit(nn_input_old.reshape(1, state.nn_input.size),
q_table,
batch_size=1,
epochs=1,
verbose=0)
# move to the next state
reward_sum += reward
steps += 1
self.log_object.chosen_action += "{0}-{1}|".format(
action_att, action_def)
# output additional data
# if default_action == -1:
# # get the attack and defense type
# type_of_att = -1
# type_of_def = -1
# if action_att != state.size_graph:
# type_of_att = int(action_att % state.size_graph_cols < state.size_graph_col1)
# if action_def != state.size_graph:
# type_of_def = int(action_att % state.size_graph_cols < state.size_graph_col1)
# # output the check one, two and three information
# self.log_object.output_string2 += ("{0}) {1} {2} : {3} {4} : {5} {6} {7} \n".format(
# steps, action_att, action_def, type_of_att, type_of_def,
# check_one.astype(int), check_three.astype(int), int(reward)))
# self.log_object.output_string2 += ("{0}) {1} : {2} {3} {4} {5} \n".format(
# steps, action_def, check_one.astype(int), check_two.astype(int),
# check_three.astype(int), int(reward)))
# # output the q_table information
# self.log_object.output_string2 += ("{0}) {1} : {2!r} \n".format(
# steps, action_def, q_table.astype(int).tolist()))
# q_table = self.model.predict(nn_input_old.reshape(1, state.nn_input.size), batch_size=1)
# self.log_object.output_string2 += ("{0}) {1} : {2!r} \n".format(
# steps, action_def, q_table.astype(int).tolist()))
# update the log object
self.log_object.reward_sum = reward_sum
self.log_object.vector_reward_sum = vector_reward_sum
self.log_object.step_count = steps
def power_iteration(lin_op,
n_points=None,
b_hat_0=None,
max_iter=1000,
tol=1e-7,
random_state=None):
"""Estimate dominant eigenvalue of linear operator A.
Parameters
----------
lin_op : callable or array
Linear operator from which we estimate the largest eigenvalue.
n_points : tuple
Input shape of the linear operator `lin_op`.
b_hat_0 : array, shape (n_points, )
Init vector. The estimated eigen-vector is stored inplace in `b_hat_0`
to allow warm start of future call of this function with the same
variable.
Returns
-------
mu_hat : float
The largest eigenvalue
"""
if hasattr(lin_op, 'dot'):
n_points = lin_op.shape[1]
lin_op = lin_op.dot
elif callable(lin_op):
msg = ("power_iteration require n_points argument when lin_op is "
"callable")
assert n_points is not None, msg
else:
raise ValueError("lin_op should be a callable or a ndarray")
rng = check_random_state(random_state)
if b_hat_0 is None:
b_hat = rng.rand(n_points)
else:
b_hat = b_hat_0
mu_hat = np.nan
for ii in range(max_iter):
b_hat = lin_op(b_hat)
norm = np.linalg.norm(b_hat)
if norm == 0:
return 0
b_hat /= norm
fb_hat = lin_op(b_hat)
mu_old = mu_hat
mu_hat = np.dot(b_hat, fb_hat)
# note, we might exit the loop before b_hat converges
# since we care only about mu_hat converging
if (mu_hat - mu_old) / mu_old < tol:
break
assert not np.isnan(mu_hat)
if b_hat_0 is not None:
# copy inplace into b_hat_0 for next call to power_iteration
np.copyto(b_hat_0, b_hat)
return mu_hat
