def renyi_information(tfr, timestamps=None, freq=None, alpha=3.0):
"""renyi_information
:param tfr:
:param timestamps:
:param freq:
:param alpha:
:type tfr:
:type timestamps:
:type freq:
:type alpha:
:return:
:rtype:
"""
if alpha == 1 and tfr.min().min() < 0:
raise ValueError("Distribution with negative values not allowed.")
m, n = tfr.shape
if timestamps is None:
timestamps = np.arange(n) + 1
elif np.allclose(timestamps, np.arange(n)):
timestamps += 1
if freq is None:
freq = np.arange(m)
freq.sort()
tfr = tfr / integrate_2d(tfr, timestamps, freq)
if alpha == 1:
R = -integrate_2d(tfr * np.log2(tfr + np.spacing(1)), timestamps, freq)
else:
R = np.log2(integrate_2d(tfr ** alpha, timestamps, freq) + np.spacing(1))
R = R / (1 - alpha)
return R
def calculateEntropy(self,Y, mship):
"""
calculates the split entropy using Y and mship (logical array) telling which
child the examples are being split into...
Input:
---------
Y: a label array
mship: (logical array) telling which child the examples are being split into, whether
each example is assigned to left split or the right one..
Returns:
---------
entropy: split entropy of the split
"""
lexam=Y[mship]
rexam=Y[np.logical_not(mship)]
pleft= len(lexam) / float(len(Y))
pright= 1-pleft
pl= stats.itemfreq(lexam)[:,1] / float(len(lexam)) + np.spacing(1)
pr= stats.itemfreq(rexam)[:,1] / float(len(rexam)) + np.spacing(1)
hl= -np.sum(pl*np.log2(pl))
hr= -np.sum(pr*np.log2(pr))
sentropy = pleft * hl + pright * hr
return sentropy
def klDiv(margDist1, margDist2):
assert isinstance(margDist1, margDistSCPP) and\
isinstance(margDist2, margDistSCPP),\
"margDist1 and margDist2 must be instances of margDistSCPP"
numpy.testing.assert_equal(margDist1.m,
margDist2.m)
kldSum = 0.0
for idx in xrange(margDist1.m):
# test that the distributions are over the same bin indices
# if they are not this calculation is meaninless
numpy.testing.assert_equal(margDist1.data[idx][1],
margDist2.data[idx][1])
#test that the distributions have the same number of bins
numpy.testing.assert_equal(len(margDist1.data[idx][0]),
len(margDist2.data[idx][0]))
m1 = margDist1.data[idx][0]
m1[m1==0] = numpy.spacing(1)
m2 = margDist2.data[idx][0]
m2[m2==0] = numpy.spacing(1)
kldSum += numpy.sum(margDist1.data[idx][0] * (numpy.log(margDist1.data[idx][0])
- numpy.log(margDist2.data[idx][0])))
return kldSum
def add_qualifying_constraint(m, coefficients, M, K, N, thetaB, g_flag, t):
''' Add the qualifying constraints to model m. The qualifying
constraint is formed by linearizing the Lagrangian with respect
to x about x0. Then linearizing that with respect to theta
about thetat. '''
qualifying_constraint = []
x = [[m.getVarByName("x_%d_%d" % (j,k)) for k in xrange(K)] for j in xrange(M)]
for i in xrange(len(coefficients)):
#calcuate the qualifying constraint formula
g_expr = gb.LinExpr()
g_expr.addConstant(coefficients[i][-1])
for j in xrange(M*K):
g_expr.add(x[j/K][j%K]* coefficients[i][j])
qualifying_constraint.append(g_expr)
#add the qualifying constraints
if g_flag[i%K][i/K] != 0:
##################### Add constraints: g_expr #########################
if thetaB[i%K][i/K] == 1:
m.addConstr(g_expr <= np.spacing(1), name="qc%d_%d_%d" % (t,k,i))
elif thetaB[i%K][i/K] == 0:
m.addConstr(g_expr >= -np.spacing(1), name="qc%d_%d_%d" % (t,k,i))
m.update()
#return qualifying constraints to calculate lagrange constraint
return qualifying_constraint
def contrast(spec, sr, a):
"""
contrast() - Compute spectral contrast
Inputs:
spec - spectrogram
sr - sample rate
a - percentage of frequencies to consider for peak and valley
Outputs:
contrasts - array of centroids for each frame
"""
nbins, nframes = spec.shape
fftSize = (nbins - 1) * 2
freqRes = sr / 2.0 / fftSize
contrasts = np.zeros(nframes)
for i in range(nframes):
sortedFreqs = np.sort(spec[:,i])
nbinsToLook = np.round(nbins * a).astype("int")
valley = np.log(np.sum(sortedFreqs[:nbinsToLook]) + np.spacing(1)) / nbinsToLook
peak = np.log(np.sum(sortedFreqs[nbins-nbinsToLook:nbins]) + np.spacing(1)) / nbinsToLook
contrasts[i] = peak - valley
return contrasts
def generateMuonPlusJet(self,nParticles=1,muonEnergy=10):
particles=["gamma","pi-","pi+","proton","pi0","e+","e-","kaon0L","kaon+","kaon-"]
config=open("config.txt","w")
self.theta=self.maxTheta + np.spacing(1.0)
while( self.theta>self.maxTheta or self.theta<0 ):
self.theta=np.random.normal(self.centralTheta,self.sigmaTheta)
eta=-math.log( math.tan(self.theta/2) )
phi=random.uniform(0,self.maxPhi)
config.write( "mu-" + " " + str(muonEnergy) + " " + str(eta) + " " + str(phi) )
for i in range(0,nParticles):
config.write("\n")
part=particles[ random.randint(0,len(particles)-1) ]
self.theta=self.maxTheta + np.spacing(1.0)
while( self.theta>self.maxTheta or self.theta<0 ):
self.theta=np.random.normal(self.centralTheta,self.sigmaTheta)
eta=-math.log( math.tan(self.theta/2) )
phi=random.uniform(0,self.maxPhi)
energy=random.uniform(self.minEnergy,self.maxEnergy)
if( eta<1.5 ):
print part + " " + str(energy) + " " + str(eta) + " " + str(phi)
phi=random.uniform(0,self.maxPhi)
config.write( part + " " + str(energy) + " " + str(eta) + " " + str(phi) )
config.close()
def entropy(self, c1, c2):
total = c1 + c2
return -1 * (
(c1 / total) * numpy.log2((c1 / total) + numpy.spacing(1))
+ (c2 / total) * numpy.log2((c2 / total) + numpy.spacing(1))
)
def colon(r1, inc, r2):
"""
Matlab's colon operator, althought it doesn't although inc is required
"""
s = np.sign(inc)
if s == 0:
return_value = np.zeros(1)
elif s == 1:
n = ((r2 - r1) + 2 * np.spacing(r2 - r1)) // inc
return_value = np.linspace(r1, r1 + inc * n, n + 1)
else: # s == -1:
# NOTE: I think this is slightly off as we start on the wrong end
# r1 should be exact, not r2
n = ((r1 - r2) + 2 * np.spacing(r1 - r2)) // np.abs(inc)
temp = np.linspace(r2, r2 + np.abs(inc) * n, n + 1)
return_value = temp[::-1]
# If the start and steps are whole numbers, we should cast as int
if(np.equal(np.mod(r1, 1), 0) and
np.equal(np.mod(s, 1), 0) and
np.equal(np.mod(r2, 1), 0)):
return return_value.astype(int)
else:
return return_value
def call(self, inputs, mask=None):
if not isinstance(inputs, list) or len(inputs) <= 1:
raise TypeError('SpkLifeLongMemory must be called on a list of tensors '
'(at least 2). Got: ' + str(inputs))
# (None(batch), 1), index of speaker
target_spk_l = inputs[0]
target_spk_l = K.reshape(target_spk_l, (target_spk_l.shape[0], ))
if K.dtype(target_spk_l) != 'int32':
target_spk_l = K.cast(target_spk_l, 'int32')
# (None(batch), embed_dim)
spk_vector_l = inputs[1]
# Start to update life-long memory based on the learned speech vector
# First do normalization
spk_vector_eps = K.switch(K.equal(spk_vector_l, 0.), np.spacing(1), spk_vector_l) # avoid zero
spk_vector_eps = K.sqrt(K.sum(spk_vector_eps**2, axis=1))
spk_vector_eps = spk_vector_eps.dimshuffle((0, 'x'))
spk_vector = T.true_div(spk_vector_l, K.repeat_elements(spk_vector_eps, self.vec_dim, axis=1))
# Store speech vector into life-long memory according to the speaker identity.
life_long_mem = T.inc_subtensor(self.life_long_mem[target_spk_l, :], spk_vector)
# Normalization for memory
life_long_mem_eps = K.switch(K.equal(life_long_mem, 0.), np.spacing(1), life_long_mem) # avoid 0
life_long_mem_eps = K.sqrt(K.sum(life_long_mem_eps**2, axis=1))
life_long_mem_eps = life_long_mem_eps.dimshuffle((0, 'x'))
life_long_mem = T.true_div(life_long_mem, K.repeat_elements(life_long_mem_eps, self.vec_dim, axis=1))
# (None(batch), spk_size, embed_dim)
return life_long_mem
def solve_master(tree, num_node, Current_node, g_flag, thetaB_list, SUBD, coefficients, xBar, thetaOpt, lamOpt, muOpt, y, cons, iteration, pool=None):
'''we solve the relaxed master problems based on thetaB_list, then select the infimum of all minimum values.
Parameters: About the tree : tree, Current_node
About the subproblem: SUBD, xBar, thetaOpt, lamOpt, muOpt, y
About the boundary: theta_L, theta_U'''
(M, N) = np.shape(y)
K = np.shape(xBar[-1])[0]
x_stor = None
Q_stor = np.inf
next_node = -1
#store all the MLBD
MLBD_stor = []
#store all the MLBD
if pool == None:
tree.nodes[Current_node].set_parameters_qualifying_constraint(lamOpt, thetaOpt, muOpt, xBar, SUBD, g_flag, coefficients)
#check whether the coefficients are already stored into the parents or not.
print ('\n%d master problems are solving...' %len(thetaB_list))
for index in xrange(len(thetaB_list)):
thetaB = thetaB_list[index].copy()
status, objVal, xOpt, thetaB, lagrangian_coefficient= solve_master_s(tree, Current_node, coefficients, thetaOpt, xBar, lamOpt, muOpt, thetaB.copy(), y, g_flag, cons)
#print objVal, xOpt
if status == 2 and objVal < SUBD - np.spacing(1):
node = tree.add_node(num_node, 0, 1, Current_node)
node.set_parameters_thetaB(thetaB, xOpt, objVal, lagrangian_coefficient)
MLBD_stor.append(objVal)
if objVal < Q_stor:
Q_stor = objVal
next_node = num_node
x_stor = xOpt
num_node = num_node + 1
else:
tree.nodes[Current_node].set_parameters_qualifying_constraint(lamOpt, thetaOpt, muOpt, xBar, SUBD, g_flag, coefficients)
len_thetaB = len(thetaB_list)
print ('\n%d master problems are solving...' %len_thetaB)
results = [pool.apply_async(solve_master_s, args = (tree, Current_node, coefficients, thetaOpt, xBar, lamOpt, muOpt, thetaB.copy(), y, g_flag, cons)) for thetaB in thetaB_list]
#put all the result into the tree.
for p in results:
#result = [status, objVal, xOpt, thetaB]
result = p.get()
if result[0] == 2 and result[1] < SUBD - np.spacing(1):
node = tree.add_node(num_node, 0, 1, Current_node)
node.set_parameters_thetaB(result[3], result[2], result[1], result[4])
#node.set_parameter(lamOpt, thetaOpt, result[3], muOpt, xBar, result[2], SUBD, result[1], g_flag, coefficients)
MLBD_stor.append(result[1])
if result[1] < Q_stor:
Q_stor = result[1]
next_node = num_node
x_stor = result[2]
num_node += 1
return x_stor, Q_stor, next_node, num_node, MLBD_stor
def _frank(M, N, alpha):
if(N<2):
raise ValueError('Dimensionality Argument [N] must be an integer >= 2')
elif(N==2):
u1 = uniform.rvs(size=M)
p = uniform.rvs(size=M)
if abs(alpha) > math.log(sys.float_info.max):
u2 = (u1 < 0).astype(int) + np.sign(alpha)*u1 # u1 or 1-u1
elif abs(alpha) > math.sqrt(np.spacing(1)):
u2 = -1*np.log((np.exp(-alpha*u1)*(1-p)/p + np.exp(-alpha))/(1 + np.exp(-alpha*u1)*(1-p)/p))/alpha
else:
u2 = p
U = np.column_stack((u1,u2))
else:
# Algorithm 1 described in both the SAS Copula Procedure, as well as the
# paper: "High Dimensional Archimedean Copula Generation Algorithm"
if(alpha<=0):
raise ValueError('For N>=3, alpha >0 in Frank Copula')
U = np.empty((M,N))
for ii in range(0,M):
p = -1.0*np.expm1(-1*alpha)
if(p==1):
# boundary case protection
p = 1 - np.spacing(1)
v = logser.rvs(p, size=1)
# sample N independent uniform random variables
x_i = uniform.rvs(size=N)
t = -1*np.log(x_i)/v
U[ii,:] = -1.0*np.log1p( np.exp(-t)*np.expm1(-1.0*alpha))/alpha
return U
def test_half_fpe(self):
oldsettings = np.seterr(all="raise")
try:
sx16 = np.array((1e-4,), dtype=float16)
bx16 = np.array((1e4,), dtype=float16)
sy16 = float16(1e-4)
by16 = float16(1e4)
# Underflow errors
assert_raises_fpe("underflow", lambda a, b: a * b, sx16, sx16)
assert_raises_fpe("underflow", lambda a, b: a * b, sx16, sy16)
assert_raises_fpe("underflow", lambda a, b: a * b, sy16, sx16)
assert_raises_fpe("underflow", lambda a, b: a * b, sy16, sy16)
assert_raises_fpe("underflow", lambda a, b: a / b, sx16, bx16)
assert_raises_fpe("underflow", lambda a, b: a / b, sx16, by16)
assert_raises_fpe("underflow", lambda a, b: a / b, sy16, bx16)
assert_raises_fpe("underflow", lambda a, b: a / b, sy16, by16)
assert_raises_fpe("underflow", lambda a, b: a / b, float16(2.0 ** -14), float16(2 ** 11))
assert_raises_fpe("underflow", lambda a, b: a / b, float16(-2.0 ** -14), float16(2 ** 11))
assert_raises_fpe("underflow", lambda a, b: a / b, float16(2.0 ** -14 + 2 ** -24), float16(2))
assert_raises_fpe("underflow", lambda a, b: a / b, float16(-2.0 ** -14 - 2 ** -24), float16(2))
assert_raises_fpe("underflow", lambda a, b: a / b, float16(2.0 ** -14 + 2 ** -23), float16(4))
# Overflow errors
assert_raises_fpe("overflow", lambda a, b: a * b, bx16, bx16)
assert_raises_fpe("overflow", lambda a, b: a * b, bx16, by16)
assert_raises_fpe("overflow", lambda a, b: a * b, by16, bx16)
assert_raises_fpe("overflow", lambda a, b: a * b, by16, by16)
assert_raises_fpe("overflow", lambda a, b: a / b, bx16, sx16)
assert_raises_fpe("overflow", lambda a, b: a / b, bx16, sy16)
assert_raises_fpe("overflow", lambda a, b: a / b, by16, sx16)
assert_raises_fpe("overflow", lambda a, b: a / b, by16, sy16)
assert_raises_fpe("overflow", lambda a, b: a + b, float16(65504), float16(17))
assert_raises_fpe("overflow", lambda a, b: a - b, float16(-65504), float16(17))
assert_raises_fpe("overflow", np.nextafter, float16(65504), float16(np.inf))
assert_raises_fpe("overflow", np.nextafter, float16(-65504), float16(-np.inf))
assert_raises_fpe("overflow", np.spacing, float16(65504))
# Invalid value errors
assert_raises_fpe("invalid", np.divide, float16(np.inf), float16(np.inf))
assert_raises_fpe("invalid", np.spacing, float16(np.inf))
assert_raises_fpe("invalid", np.spacing, float16(np.nan))
assert_raises_fpe("invalid", np.nextafter, float16(np.inf), float16(0))
assert_raises_fpe("invalid", np.nextafter, float16(-np.inf), float16(0))
assert_raises_fpe("invalid", np.nextafter, float16(0), float16(np.nan))
# These should not raise
float16(65472) + float16(32)
float16(2 ** -13) / float16(2)
float16(2 ** -14) / float16(2 ** 10)
np.spacing(float16(-65504))
np.nextafter(float16(65504), float16(-np.inf))
np.nextafter(float16(-65504), float16(np.inf))
float16(2 ** -14) / float16(2 ** 10)
float16(-2 ** -14) / float16(2 ** 10)
float16(2 ** -14 + 2 ** -23) / float16(2)
float16(-2 ** -14 - 2 ** -23) / float16(2)
finally:
np.seterr(**oldsettings)
def interior_point_using_distance(coefficients, nCuts, dim, marker, weight, index = -1):
''' Get the interior point from the certain region described by marker. The returned point should be far from all the hyperplanes.
coefficients: the coefficients of hyperplanes with the same order. For example, (a1, a2, a3, c) in the hyperplane (a1 * x1 + a2 * x2 + a3 * x3 + c = 0)
nCuts: the number of the hyerplanes.
Dim: the dimension of the space.
marker: the sign of the hyperplane.
weight: calcualte the norm 2 of the coefficients except the constant of all hyperplanes
index: the hyperplane we want to trim. Usually index = -1, which means no hyperplane will be trimed.
Here, we add the different weights for different hyperplanes when we calculate the distance.
maximize z
subject to A*x + b + weight*z <= np.spacing(1), for all sign >=0
-1*(A*x + b) + weight*z <= np.spacing(1), for all sign <= 0
'''
#Create a new model
m = gb.Model('Interior_point')
#Set parameters
m.setParam('OutputFlag', False)
x = [0 for i in xrange(dim)]
for i in xrange(dim):
x[i] = m.addVar(lb = -1e7, ub = 1e7, vtype=gb.GRB.CONTINUOUS, name = 'x_%d'%(i))
obj = m.addVar(lb = 0.0, vtype = gb.GRB.CONTINUOUS, name = 'objective')
m.update()
for i in xrange(nCuts):
#hyperplane index is trimed, so there is no constraint for index hyperplane.
if index != i:
g_expr = gb.LinExpr()
g_expr.addConstant(coefficients[i][-1])
for j in xrange(dim):
g_expr.add(x[j] * coefficients[i][j])
if marker[i] == 0:
m.addConstr(-1 * g_expr + weight[i]*obj <= np.spacing(0), name = 'qc_%d' %(i))
elif marker[i] == 1:
m.addConstr(g_expr + weight[i]*obj <= np.spacing(0), name = 'qc_%d' %(i))
m.update()
#Create the objective : maximize obj
m.setObjective(obj, gb.GRB.MAXIMIZE)
m.update()
#Optimize the test problem.
try:
m.optimize()
except gb.GurobiError as e:
print e.message
if m.Status == gb.GRB.OPTIMAL:
xOpt = np.empty(dim)
for i in xrange(dim):
xOpt[i] = x[i].x
return m.Status, xOpt, obj.x
else:
return m.Status, np.nan, np.nan
def ellipse_axis_length( a ):
b,c,d,f,g,a = a[1]/2, a[2], a[3]/2, a[4]/2, a[5], a[0]
up = 2*(a*f*f+c*d*d+g*b*b-2*b*d*f-a*c*g)
down1=(b*b-a*c)*( (c-a)*np.sqrt(1+4*b*b/((a-c)*(a-c)))-(c+a))
down2=(b*b-a*c)*( (a-c)*np.sqrt(1+4*b*b/((a-c)*(a-c)))-(c+a))
res1=np.sqrt(up/(np.abs(down1)+np.spacing(1)))
res2=np.sqrt(up/(np.abs(down2)+np.spacing(1)))
return np.array([res1, res2])
def estimate_parameters(left_stft, right_stft, stft_window_length): #List comprehensions are amazing! w_range = [i for j in (range(1, stft_window_length/2+1), range(-stft_window_length/2 + 1, 0)) for i in j] w = np.array([2*np.pi*i/(stft_window_length) for i in w_range if i is not 0]) delay_estimation = -1/w * np.angle((right_stft + np.spacing(1))/(left_stft + np.spacing(1))) attenuation_estimation = np.absolute(right_stft/left_stft) - np.absolute(left_stft/right_stft) return attenuation_estimation, delay_estimation
def _evenly_spaced_condition_num_helper(self, p_order):
import numpy as np
x_vals = np.linspace(-1, 1, p_order + 1)
leg_mat = self._call_func_under_test(x_vals)
kappa2 = np.linalg.cond(leg_mat, p=2)
# This gives the exponent of kappa2.
base_exponent = np.log2(np.spacing(1.0))
return int(np.round(np.log2(np.spacing(kappa2)) - base_exponent))
def lanczos2(x):
# f = (sin(pi*x) .* sin(pi*x/2) + eps) ./ ((pi^2 * x.^2 / 2) + eps);
# f = f .* (abs(x) < 2);
result = (np.sin(np.pi * x) * np.sin(np.pi * x / 2 + np.spacing(1))) / (
(np.power(np.pi, 2) * np.power(x, 2) / 2) + np.spacing(1)
)
print (np.abs(result) < 2)
result = result * (np.abs(result) < 2)
return result
def t_calc_information(p_x_given_t, PYgivenTs, PXs, PYs):
"""Calculate the MI - I(X;T) and I(Y;T)"""
Hx = np.nansum(-np.dot(PXs, np.log2(PXs)))
Hxt = - np.nansum((np.dot(np.multiply(p_x_given_t, np.log2(p_x_given_t)), PXs)))
Hyt = - np.nansum(np.dot(PYgivenTs*np.log2(PYgivenTs+np.spacing(1)), PTs))
Hy = np.nansum(-PYs * np.log2(PYs+np.spacing(1)))
IYT = Hy - Hyt
ITX = Hx - Hxt
return ITX, IYT
def checkResult(Lbar,eigvec,eigval,k):
"""
"input
"matrix Lbar and k eig values and k eig vectors
"print norm(Lbar*eigvec[:,i]-lamda[i]*eigvec[:,i])
"""
check=[np.dot(Lbar,eigvec[:,i])-eigval[i]*eigvec[:,i] for i in range(0,k)]
length=[np.linalg.norm(e) for e in check]/np.spacing(1)
print("Lbar*v-lamda*v are %s*%s" % (length,np.spacing(1)))
def test_segmentation_utils():
ctx = mx.context.current_context()
import os
if not os.path.isdir(os.path.expanduser('~/.mxnet/datasets/voc')):
return
transform_fn = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([.485, .456, .406], [.229, .224, .225])
])
# get the dataset
# TODO FIXME: change it to ADE20K dataset and pretrained model
dataset = ADE20KSegmentation(split='val')
# load pretrained net
net = gluoncv.model_zoo.get_model('fcn_resnet50_ade', pretrained=True, ctx=ctx)
# count for pixAcc and mIoU
total_inter, total_union, total_correct, total_label = 0, 0, 0, 0
np_inter, np_union, np_correct, np_label = 0, 0, 0, 0
tbar = tqdm(range(10))
for i in tbar:
img, mask = dataset[i]
# prepare data and make prediction
img = transform_fn(img)
img = img.expand_dims(0).as_in_context(ctx)
mask = mask.expand_dims(0)
pred = net.evaluate(img).as_in_context(mx.cpu(0))
# gcv prediction
correct1, labeled1 = batch_pix_accuracy(pred, mask)
inter1, union1 = batch_intersection_union(pred, mask, dataset.num_class)
total_correct += correct1
total_label += labeled1
total_inter += inter1
total_union += union1
pixAcc = 1.0 * total_correct / (np.spacing(1) + total_label)
IoU = 1.0 * total_inter / (np.spacing(1) + total_union)
mIoU = IoU.mean()
# np predicition
pred2 = np.argmax(pred.asnumpy().astype('int64'), 1) + 1
mask2 = mask.squeeze().asnumpy().astype('int64') + 1
_, correct2, labeled2 = pixelAccuracy(pred2, mask2)
inter2, union2 = intersectionAndUnion(pred2, mask2, dataset.num_class)
np_correct += correct2
np_label += labeled2
np_inter += inter2
np_union += union2
np_pixAcc = 1.0 * np_correct / (np.spacing(1) + np_label)
np_IoU = 1.0 * np_inter / (np.spacing(1) + np_union)
np_mIoU = np_IoU.mean()
tbar.set_description('pixAcc: %.3f, np_pixAcc: %.3f, mIoU: %.3f, np_mIoU: %.3f'%\
(pixAcc, np_pixAcc, mIoU, np_mIoU))
np.testing.assert_allclose(total_inter, np_inter)
np.testing.assert_allclose(total_union, np_union)
np.testing.assert_allclose(total_correct, np_correct)
np.testing.assert_allclose(total_label, np_label)
def My_Angle(u,v):
du = sqrt(sum(u**2))
dv = sqrt(sum(v**2))
du = max(du,spacing(1))
dv = max(dv,spacing(1))
x = sum(u*v) / (du*dv)
x = clamp(x, -1.0, 1.0)
angle = acos(x)
return angle
def test_spacing():
for t in [np.float32, np.float64, np.longdouble]:
one = t(1)
eps = np.finfo(t).eps
nan = t(np.nan)
inf = t(np.inf)
assert np.spacing(one) == eps
assert np.isnan(np.spacing(nan))
assert np.isnan(np.spacing(inf))
assert np.isnan(np.spacing(-inf))
def _test_spacing(t):
one = t(1)
eps = np.finfo(t).eps
nan = t(np.nan)
inf = t(np.inf)
assert np.spacing(one) == eps
assert np.isnan(np.spacing(nan))
assert np.isnan(np.spacing(inf))
assert np.isnan(np.spacing(-inf))
assert np.spacing(t(1e30)) != 0
def calc_information(probTgivenXs, PYgivenTs, PXs, PYs):
"""Calculate the MI - I(X;T) and I(Y;T)"""
PTs = np.nansum(probTgivenXs*PXs, axis=1)
Ht = np.nansum(-np.dot(PTs, np.log2(PTs)))
Htx = - np.nansum((np.dot(np.multiply(probTgivenXs, np.log2(probTgivenXs)), PXs)))
Hyt = - np.nansum(np.dot(PYgivenTs*np.log2(PYgivenTs+np.spacing(1)), PTs))
Hy = np.nansum(-PYs * np.log2(PYs+np.spacing(1)))
IYT = Hy - Hyt
ITX = Ht - Htx
return ITX, IYT
def is_converged(fval, prev_fval, thresh=1e-4, warn=False):
"""Check if an objective function has converged.
Parameters
----------
fval : float
The current value.
prev_fval : float
The previous value.
thresh : float
The convergence threshold.
warn : bool
Flag indicating whether to warn the user when the
fval decreases.
Returns
-------
bool :
Flag indicating whether the objective function has converged or not.
Notes
-----
The test returns true if the slope of the function falls below the threshold; i.e.
.. math::
\\frac{|f(t) - f(t-1)|}{\\text{avg}} < \\text{thresh},
where
.. math::
\\text{avg} = \\frac{|f(t)| + |f(t+1)|}{2}
.. note::
Ported from Matlab:
| Project: `Probabilistic Modeling Toolkit for Matlab/Octave <https://github.com/probml/pmtk3>`_.
| Copyright (2010) Kevin Murphy and Matt Dunham
| License: `MIT <https://github.com/probml/pmtk3/blob/5fefd068a2e84ae508684d3e4750bd72a4164ba0/license.txt>`_
"""
converged = False
delta_fval = abs(fval - prev_fval)
# noinspection PyTypeChecker
avg_fval = np.true_divide(abs(fval) + abs(prev_fval) + np.spacing(1), 2)
if float(delta_fval) / float(avg_fval) < thresh:
converged = True
if warn and (fval - prev_fval) < -2 * np.spacing(1):
print("is_converged: fval decrease, objective decreased!")
return converged
def _chebyshev_conditioning_helper(self, num_nodes):
import numpy as np
theta = np.pi * np.arange(2 * num_nodes - 1, 0, -2,
dtype=np.float64) / (2 * num_nodes)
x_vals = np.cos(theta)
leg_mat = self._call_func_under_test(x_vals)
kappa2 = np.linalg.cond(leg_mat, p=2)
# This gives the exponent of kappa2.
base_exponent = np.log2(np.spacing(1.0))
return int(np.round(np.log2(np.spacing(kappa2)) - base_exponent))
def _test_spacing(t):
one = t(1)
eps = np.finfo(t).eps
nan = t(np.nan)
inf = t(np.inf)
with np.errstate(invalid='ignore'):
assert_(np.spacing(one) == eps)
assert_(np.isnan(np.spacing(nan)))
assert_(np.isnan(np.spacing(inf)))
assert_(np.isnan(np.spacing(-inf)))
assert_(np.spacing(t(1e30)) != 0)
def forward_problem_hessian(theta, N):
""" Gets the etas for given thetas. Here a costum-made iterative solver is
used.
:param numpy.ndarray theta:
(d,)-dimensional array containing all thetas
:param int N:
Number of cells
:returns:
(d,) numpy.ndarray with all etas.
"""
# Initialize eta vector
eta = numpy.empty(theta.shape)
eta_max = 0.5*numpy.ones(N)
# Extract first order thetas
theta1 = theta[:N]
# Get indices
triu_idx = numpy.triu_indices(N, k=1)
diag_idx = numpy.diag_indices(N)
# Write second order thetas into matrix
theta2 = numpy.zeros([N, N])
theta2[triu_idx] = theta[N:]
theta2 += theta2.T
conv = numpy.inf
# Solve self-consistent equations and calculate approximation of
# fisher matrix
iter_num = 0
while conv > 1e-4 and iter_num < 500:
deta = self_consistent_eq(eta_max, theta1=theta1, theta2=theta2,
expansion='TAP')
Hinv = self_consistent_eq_Hinv(eta_max, theta1=theta1, theta2=theta2,
expansion='TAP')
eta_max -= .1*numpy.dot(Hinv, deta)
conv = numpy.amax(numpy.absolute(deta))
iter_num += 1
eta_max[eta_max <= 0.] = numpy.spacing(1)
eta_max[eta_max >= 1.] = 1. - numpy.spacing(1)
if iter_num == 500:
raise Exception('Self consistent equations could not be solved!')
G_inv = - theta2 - theta2**2*numpy.outer(0.5 - eta_max[:N],
0.5 - eta_max[:N])
G_inv[diag_idx] = 1./eta_max + 1./(1.-eta_max) + .5*numpy.dot(theta2**2,
(eta_max -
eta_max**2))
G = numpy.linalg.inv(G_inv)
# Compute second order eta
eta2 = G + numpy.outer(eta_max[:N], eta_max[:N])
eta[N:] = eta2[triu_idx]
eta[:N] = eta_max
eta[eta < 0.] = numpy.spacing(1)
eta[eta > 1.] = 1. - numpy.spacing(1)
return eta
def get(self):
"""Gets the current evaluation result.
Returns
-------
metrics : tuple of float
pixAcc and mIoU
"""
pixAcc = 1.0 * self.total_correct / (np.spacing(1) + self.total_label)
IoU = 1.0 * self.total_inter / (np.spacing(1) + self.total_union)
mIoU = IoU.mean()
return pixAcc, mIoU
def allr( w, lPx_all, cls, y, lamb ):
lPx_pos = lPx_all[cls,:,y == (cls+1)].T
lPx_neg = lPx_all[cls,:,y != (cls+1)].T
P = ( numpy.log( numpy.spacing(1) + (numpy.exp( lPx_pos )*w[None,:,None] ).sum(1) / \
( ( numpy.exp( lPx_all[:,:,y == (cls+1)] )*w[None,:,None] ).sum(1)).sum(0) )).sum() / (y == (cls+1)).sum(0)
N = ( numpy.log( numpy.spacing(1) + (numpy.exp( lPx_neg )*w[None,:,None] ).sum(1) / \
( ( numpy.exp( lPx_all[:,:,y != (cls+1)] )*w[None,:,None] ).sum(1)).sum(0) )).sum() / (y != (cls+1)).sum(0)
return -( P-N + lamb*((w**2).sum()) )
poly.append(np.sqrt((2*len(b) - 1) / 2) * igral)
result.append(np.array(poly))
return result
def calc_V(x, n):
V = [np.ones(x.shape), x.copy()]
for i in range(2, n):
V.append((2*i-1) / i * x * V[-1] - (i-1) / i * V[-2])
for i in range(n):
V[i] *= np.sqrt(i + 0.5)
return np.array(V).T
eps = np.spacing(1)
# the nodes and Newton polynomials
ns = (5, 9, 17, 33)
xi = [-np.cos(np.linspace(0, np.pi, n)) for n in ns]
# Make `xi` perfectly anti-symmetric, important for splitting the intervals
xi = [(row - row[::-1]) / 2 for row in xi]
# Compute the Vandermonde-like matrix and its inverse.
V = [calc_V(x, n) for x, n in zip(xi, ns)]
V_inv = list(map(scipy.linalg.inv, V))
Vcond = [scipy.linalg.norm(a, 2) * scipy.linalg.norm(b, 2) for a, b in zip(V, V_inv)]
# Compute the shift matrices.
T_left, T_right = [V_inv[3] @ calc_V((xi[3] + a) / 2, ns[3]) for a in [-1, 1]]
def corner_subpix(image, corners, window_size=11, alpha=0.99):
"""Determine subpixel position of corners.
A statistical test decides whether the corner is defined as the
intersection of two edges or a single peak. Depending on the classification
result, the subpixel corner location is determined based on the local
covariance of the grey-values. If the significance level for either
statistical test is not sufficient, the corner cannot be classified, and
the output subpixel position is set to NaN.
Parameters
----------
image : ndarray
Input image.
corners : (N, 2) ndarray
Corner coordinates `(row, col)`.
window_size : int, optional
Search window size for subpixel estimation.
alpha : float, optional
Significance level for corner classification.
Returns
-------
positions : (N, 2) ndarray
Subpixel corner positions. NaN for "not classified" corners.
References
----------
.. [1] Förstner, W., & Gülch, E. (1987, June). A fast operator for detection and
precise location of distinct points, corners and centres of circular
features. In Proc. ISPRS intercommission conference on fast processing of
photogrammetric data (pp. 281-305).
https://cseweb.ucsd.edu/classes/sp02/cse252/foerstner/foerstner.pdf
.. [2] https://en.wikipedia.org/wiki/Corner_detection
Examples
--------
>>> from skimage.feature import corner_harris, corner_peaks, corner_subpix
>>> img = np.zeros((10, 10))
>>> img[:5, :5] = 1
>>> img[5:, 5:] = 1
>>> img.astype(int)
array([[1, 1, 1, 1, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 1, 1, 1, 1, 1]])
>>> coords = corner_peaks(corner_harris(img), min_distance=2,
... threshold_rel=0)
>>> coords_subpix = corner_subpix(img, coords, window_size=7)
>>> coords_subpix
array([[4.5, 4.5]])
"""
# window extent in one direction
wext = (window_size - 1) // 2
image = np.pad(image, pad_width=wext, mode='constant', constant_values=0)
# add pad width, make sure to not modify the input values in-place
corners = safe_as_int(corners + wext)
# normal equation arrays
N_dot = np.zeros((2, 2), dtype=np.double)
N_edge = np.zeros((2, 2), dtype=np.double)
b_dot = np.zeros((2, ), dtype=np.double)
b_edge = np.zeros((2, ), dtype=np.double)
# critical statistical test values
redundancy = window_size**2 - 2
t_crit_dot = stats.f.isf(1 - alpha, redundancy, redundancy)
t_crit_edge = stats.f.isf(alpha, redundancy, redundancy)
# coordinates of pixels within window
y, x = np.mgrid[-wext:wext + 1, -wext:wext + 1]
corners_subpix = np.zeros_like(corners, dtype=np.double)
for i, (y0, x0) in enumerate(corners):
# crop window around corner + border for sobel operator
miny = y0 - wext - 1
maxy = y0 + wext + 2
minx = x0 - wext - 1
maxx = x0 + wext + 2
window = image[miny:maxy, minx:maxx]
winx, winy = _compute_derivatives(window, mode='constant', cval=0)
# compute gradient suares and remove border
winx_winx = (winx * winx)[1:-1, 1:-1]
winx_winy = (winx * winy)[1:-1, 1:-1]
winy_winy = (winy * winy)[1:-1, 1:-1]
# sum of squared differences (mean instead of gaussian filter)
Axx = np.sum(winx_winx)
Axy = np.sum(winx_winy)
Ayy = np.sum(winy_winy)
# sum of squared differences weighted with coordinates
# (mean instead of gaussian filter)
bxx_x = np.sum(winx_winx * x)
bxx_y = np.sum(winx_winx * y)
bxy_x = np.sum(winx_winy * x)
bxy_y = np.sum(winx_winy * y)
byy_x = np.sum(winy_winy * x)
byy_y = np.sum(winy_winy * y)
# normal equations for subpixel position
N_dot[0, 0] = Axx
N_dot[0, 1] = N_dot[1, 0] = -Axy
N_dot[1, 1] = Ayy
N_edge[0, 0] = Ayy
N_edge[0, 1] = N_edge[1, 0] = Axy
N_edge[1, 1] = Axx
b_dot[:] = bxx_y - bxy_x, byy_x - bxy_y
b_edge[:] = byy_y + bxy_x, bxx_x + bxy_y
# estimated positions
try:
est_dot = np.linalg.solve(N_dot, b_dot)
est_edge = np.linalg.solve(N_edge, b_edge)
except np.linalg.LinAlgError:
# if image is constant the system is singular
corners_subpix[i, :] = np.nan, np.nan
continue
# residuals
ry_dot = y - est_dot[0]
rx_dot = x - est_dot[1]
ry_edge = y - est_edge[0]
rx_edge = x - est_edge[1]
# squared residuals
rxx_dot = rx_dot * rx_dot
rxy_dot = rx_dot * ry_dot
ryy_dot = ry_dot * ry_dot
rxx_edge = rx_edge * rx_edge
rxy_edge = rx_edge * ry_edge
ryy_edge = ry_edge * ry_edge
# determine corner class (dot or edge)
# variance for different models
var_dot = np.sum(winx_winx * ryy_dot - 2 * winx_winy * rxy_dot +
winy_winy * rxx_dot)
var_edge = np.sum(winy_winy * ryy_edge + 2 * winx_winy * rxy_edge +
winx_winx * rxx_edge)
# test value (F-distributed)
if var_dot < np.spacing(1) and var_edge < np.spacing(1):
t = np.nan
elif var_dot == 0:
t = np.inf
else:
t = var_edge / var_dot
# 1 for edge, -1 for dot, 0 for "not classified"
corner_class = int(t < t_crit_edge) - int(t > t_crit_dot)
if corner_class == -1:
corners_subpix[i, :] = y0 + est_dot[0], x0 + est_dot[1]
elif corner_class == 0:
corners_subpix[i, :] = np.nan, np.nan
elif corner_class == 1:
corners_subpix[i, :] = y0 + est_edge[0], x0 + est_edge[1]
# subtract pad width
corners_subpix -= wext
return corners_subpix
def test_ufunc_spacing_c(A: dace.complex64[10]):
return np.spacing(A)
def decomposition_criteria(Se, Sr, noise):
r"""
Computes the SDR of each couple reference/estimate sources.
Inputs
------
Se: numpy array
estimateof column signals.
Sr: numpy array
reference of column signals.
noise: numpy array
noise matrix cotaminating the data.
Outputs
------
criteria: dict
value of each of the criteria.
decomposition: dict
decomposition of the estimated sources under target, interference,
noise and artifacts.
"""
# compute projections
Sr = Sr / core.tools.dim_norm(Sr, 0)
pS = Sr.dot(np.linalg.lstsq(Sr, Se)[0])
SN = np.hstack([Sr, noise])
#pSN = SN.dot(np.linalg.lstsq(SN, Se)[0]) # this crashes on MAC
pSN = SN.dot(linalg.lstsq(SN, Se)[0])
eps = np.spacing(1)
# compute decompositions
decomposition = {}
# targets
decomposition['target'] = np.sum(Se * Sr, 0) * Sr # Sr is normalized
# interferences
decomposition['interferences'] = pS - decomposition['target']
# noise
decomposition['noise'] = pSN - pS
# artifacts
decomposition['artifacts'] = Se - pSN
# compute criteria
criteria = {}
# SDR: source to distortion ratio
num = decomposition['target']
den = decomposition['interferences'] +\
(decomposition['noise'] + decomposition['artifacts'])
norm_num_2 = np.sum(num * num, 0)
norm_den_2 = np.sum(den * den, 0)
criteria['SDR'] = np.mean(
10 *
np.log10(np.maximum(norm_num_2, eps) / np.maximum(norm_den_2, eps)))
criteria['SDR median'] = np.median(
10 *
np.log10(np.maximum(norm_num_2, eps) / np.maximum(norm_den_2, eps)))
# SIR: source to interferences ratio
num = decomposition['target']
den = decomposition['interferences']
norm_num_2 = sum(num * num, 0)
norm_den_2 = sum(den * den, 0)
criteria['SIR'] = np.mean(
10 *
np.log10(np.maximum(norm_num_2, eps) / np.maximum(norm_den_2, eps)))
# SNR: source to noise ratio
if np.max(np.abs(noise)) > 0: # only if there is noise
num = decomposition['target'] + decomposition['interferences']
den = decomposition['noise']
norm_num_2 = sum(num * num, 0)
norm_den_2 = sum(den * den, 0)
criteria['SNR'] = np.mean(10 * np.log10(
np.maximum(norm_num_2, eps) / np.maximum(norm_den_2, eps)))
else:
criteria['SNR'] = np.inf
# SAR: sources to artifacts ratio
if (noise.shape[1] + Sr.shape[1] < Sr.shape[0]):
# if noise + sources form a basis, there is no "artifacts"
num = decomposition['target'] +\
(decomposition['interferences'] + decomposition['noise'])
den = decomposition['artifacts']
norm_num_2 = sum(num * num, 0)
norm_den_2 = sum(den * den, 0)
criteria['SAR'] = np.mean(10 * np.log10(
np.maximum(norm_num_2, eps) / np.maximum(norm_den_2, eps)))
else:
criteria['SAR'] = np.inf
return (criteria, decomposition)
def __init__(self, dataset='ansim', ov=3, split=1, nfft=1024, db=30, wav_extra_name='', desc_extra_name=''):
# TODO: Change the path according to your machine.
# TODO: It should point to a folder which consists of sub-folders for audio and metada
if dataset == 'ansim':
self._base_folder = 'data/' #os.path.join('/scratch/asignal/sharath', 'doa_data/')
elif dataset == 'resim':
self._base_folder = os.path.join('/proj/asignal/TUT_SELD/', 'doa_data_echoic/')
elif dataset == 'cansim':
self._base_folder = os.path.join('/proj/asignal/TUT_SELD/', 'doa_circdata/')
elif dataset == 'cresim':
self._base_folder = os.path.join('/proj/asignal/TUT_SELD/', 'doa_circdata_echoic/')
elif dataset == 'real':
self._base_folder = os.path.join('/proj/asignal/TUT_SELD/', 'tut_seld_data/')
elif dataset == 'mansim':
self._base_folder = os.path.join('/proj/asignal/TUT_SELD/', 'moving_sound_events_foa/')
elif dataset == 'mreal':
self._base_folder = os.path.join('/proj/asignal/TUT_SELD/', 'tut_seld_movingdata_foa/')
# Input directories
self._aud_dir = os.path.join(self._base_folder, 'wav_ov{}_split{}_{}db{}'.format(ov, split, db, wav_extra_name))
self._desc_dir = os.path.join(self._base_folder, 'desc_ov{}_split{}{}'.format(ov, split, desc_extra_name))
# Output directories
self._label_dir = None
self._feat_dir = None
self._feat_dir_norm = None
# Local parameters
self._mode = None
self._ov = ov
self._split = split
self._db = db
self._nfft = nfft
self._win_len = self._nfft
self._hop_len = self._nfft//2
self._dataset = dataset
self._eps = np.spacing(np.float(1e-16))
# If circular-array 8 channels else 4 for Ambisonic
if 'c' in self._dataset:
self._nb_channels = 8
else:
self._nb_channels = 4
# Sound event classes dictionary
self._unique_classes = dict()
if 'real' in self._dataset:
# Urbansound8k sound events
self._unique_classes = \
{
'1': 0,
'3': 1,
'4': 2,
'5': 3,
'6': 4,
'7': 5,
'8': 6,
'9': 7
}
else:
# DCASE 2016 Task 2 sound events
self._unique_classes = \
{
'clearthroat': 2,
'cough': 8,
'doorslam': 9,
'drawer': 1,
'keyboard': 6,
'keysDrop': 4,
'knock': 0,
'laughter': 10,
'pageturn': 7,
'phone': 3,
'speech': 5
}
self._fs = 44100
self._frame_res = self._fs / float(self._hop_len)
self._hop_len_s = self._nfft/2.0/self._fs
self._nb_frames_1s = int(1 / self._hop_len_s)
self._fade_win_size = 0.01 * self._fs
self._resolution = 10
self._azi_list = range(-180, 180, self._resolution)
self._length = len(self._azi_list)
self._ele_list = range(-60, 60, self._resolution)
self._height = len(self._ele_list)
self._weakness = None
# For regression task only
self._default_azi = 180
self._default_ele = 60
if self._default_azi in self._azi_list:
print('ERROR: chosen default_azi value {} should not exist in azi_list'.format(self._default_azi))
exit()
if self._default_ele in self._ele_list:
print('ERROR: chosen default_ele value {} should not exist in ele_list'.format(self._default_ele))
exit()
self._audio_max_len_samples = 30 * self._fs # TODO: Fix the audio synthesis code to always generate 30s of
# audio. Currently it generates audio till the last active sound event, which is not always 30s long. This is a
# quick fix to overcome that. We need this because, for processing and training we need the length of features
# to be fixed.
self._max_frames = int(np.ceil((self._audio_max_len_samples - self._win_len) / float(self._hop_len)))
def setup_train(self):
# dimensions: (batch, time, notes, input_data) with input_data as in architecture
self.input_mat = T.btensor4()
# dimensions: (batch, time, notes, onOrArtic) with 0:on, 1:artic
self.output_mat = T.btensor4()
self.epsilon = np.spacing(np.float32(1.0))
def step_time(in_data, *other):
other = list(other)
split = -len(self.t_layer_sizes) if self.dropout else len(other)
hiddens = other[:split]
masks = [None] + other[split:] if self.dropout else []
new_states = self.time_model.forward(in_data,
prev_hiddens=hiddens,
dropout=masks)
return new_states
def step_note(in_data, *other):
other = list(other)
split = -len(self.p_layer_sizes) if self.dropout else len(other)
hiddens = other[:split]
masks = [None] + other[split:] if self.dropout else []
new_states = self.pitch_model.forward(in_data,
prev_hiddens=hiddens,
dropout=masks)
return new_states
# We generate an output for each input, so it doesn't make sense to use the last output as an input.
# Note that we assume the sentinel start value is already present
# TEMP CHANGE: NO SENTINEL
input_slice = self.input_mat[:, 0:-1]
n_batch, n_time, n_note, n_ipn = input_slice.shape
# time_inputs is a matrix (time, batch/note, input_per_note)
time_inputs = input_slice.transpose((1, 0, 2, 3)).reshape(
(n_time, n_batch * n_note, n_ipn))
num_time_parallel = time_inputs.shape[1]
# apply dropout
if self.dropout > 0:
time_masks = theano_lstm.MultiDropout(
[(num_time_parallel, shape) for shape in self.t_layer_sizes],
self.dropout)
else:
time_masks = []
time_outputs_info = [
initial_state_with_taps(layer, num_time_parallel)
for layer in self.time_model.layers
]
time_result, _ = theano.scan(fn=step_time,
sequences=[time_inputs],
non_sequences=time_masks,
outputs_info=time_outputs_info)
self.time_thoughts = time_result
# Now time_result is a list of matrix [layer](time, batch/note, hidden_states) for each layer but we only care about
# the hidden state of the last layer.
# Transpose to be (note, batch/time, hidden_states)
last_layer = get_last_layer(time_result)
n_hidden = last_layer.shape[2]
time_final = get_last_layer(time_result).reshape(
(n_time, n_batch, n_note, n_hidden)).transpose(
(2, 1, 0, 3)).reshape((n_note, n_batch * n_time, n_hidden))
# note_choices_inputs represents the last chosen note. Starts with [0,0], doesn't include last note.
# In (note, batch/time, 2) format
# Shape of start is thus (1, N, 2), concatenated with all but last element of output_mat transformed to (x, N, 2)
start_note_values = T.alloc(0, 1, time_final.shape[1], 2)
correct_choices = self.output_mat[:, 1:, 0:-1, :].transpose(
(2, 0, 1, 3)).reshape((n_note - 1, n_batch * n_time, 2))
note_choices_inputs = T.concatenate(
[start_note_values, correct_choices], axis=0)
# Together, this and the output from the last LSTM goes to the new LSTM, but rotated, so that the batches in
# one direction are the steps in the other, and vice versa.
note_inputs = T.concatenate([time_final, note_choices_inputs], axis=2)
num_timebatch = note_inputs.shape[1]
# apply dropout
if self.dropout > 0:
pitch_masks = theano_lstm.MultiDropout(
[(num_timebatch, shape) for shape in self.p_layer_sizes],
self.dropout)
else:
pitch_masks = []
note_outputs_info = [
initial_state_with_taps(layer, num_timebatch)
for layer in self.pitch_model.layers
]
note_result, _ = theano.scan(fn=step_note,
sequences=[note_inputs],
non_sequences=pitch_masks,
outputs_info=note_outputs_info)
self.note_thoughts = note_result
# Now note_result is a list of matrix [layer](note, batch/time, onOrArticProb) for each layer but we only care about
# the hidden state of the last layer.
# Transpose to be (batch, time, note, onOrArticProb)
note_final = get_last_layer(note_result).reshape(
(n_note, n_batch, n_time, 2)).transpose(1, 2, 0, 3)
# The cost of the entire procedure is the negative log likelihood of the events all happening.
# For the purposes of training, if the ouputted probability is P, then the likelihood of seeing a 1 is P, and
# the likelihood of seeing 0 is (1-P). So the likelihood is (1-P)(1-x) + Px = 2Px - P - x + 1
# Since they are all binary decisions, and are all probabilities given all previous decisions, we can just
# multiply the likelihoods, or, since we are logging them, add the logs.
# Note that we mask out the articulations for those notes that aren't played, because it doesn't matter
# whether or not those are articulated.
# The padright is there because self.output_mat[:,:,:,0] -> 3D matrix with (b,x,y), but we need 3d tensor with
# (b,x,y,1) instead
active_notes = T.shape_padright(self.output_mat[:, 1:, :, 0])
mask = T.concatenate([T.ones_like(active_notes), active_notes], axis=3)
loglikelihoods = mask * T.log(2 * note_final * self.output_mat[:, 1:] -
note_final - self.output_mat[:, 1:] + 1 +
self.epsilon)
self.cost = T.neg(T.sum(loglikelihoods))
updates, _, _, _, _ = create_optimization_updates(self.cost,
self.params,
method="adadelta")
self.update_fun = theano.function(
inputs=[self.input_mat, self.output_mat],
outputs=self.cost,
updates=updates,
allow_input_downcast=True)
self.update_thought_fun = theano.function(
inputs=[self.input_mat, self.output_mat],
outputs=ensure_list(self.time_thoughts) +
ensure_list(self.note_thoughts) + [self.cost],
allow_input_downcast=True)
def _ars_compute_hulls(S, fS, domain):
# compute lower piecewise-linear hull
# if the domain of func is unbounded to the left or right, then the lower
# hull takes on -inf to the left or right of the end points of S
lowerHull = []
for li in np.arange(len(S) - 1):
h = Hull()
h.m = (fS[li + 1] - fS[li]) / (S[li + 1] - S[li])
h.b = fS[li] - h.m * S[li]
h.left = S[li]
h.right = S[li + 1]
lowerHull.append(h)
# compute upper piecewise-linear hull
upperHull = []
if np.isinf(domain[0]):
# first line (from -infinity)
m = (fS[1] - fS[0]) / (S[1] - S[0])
b = fS[0] - m * S[0]
# pro = np.exp(b)/m * ( np.exp(m*S[0]) - 0 ) # integrating in from -infinity
lnpr = b - np.log(m) + m * S[0]
h = Hull()
h.m = m
h.b = b
h.lnpr = lnpr
h.left = -np.Inf
h.right = S[0]
upperHull.append(h)
# second line
m = (fS[2] - fS[1]) / (S[2] - S[1])
b = fS[1] - m * S[1]
# pro = np.exp(b)/m * ( np.exp(m*S[1]) - np.exp(m*S[0]) )
lnpr = _signed_lse(m, b, S[1], S[0])
# Append upper hull for second line
h = Hull()
h.m = m
h.b = b
h.lnpr = lnpr
h.left = S[0]
h.right = S[1]
upperHull.append(h)
# interior lines
# there are two lines between each abscissa
for li in np.arange(1, len(S) - 2):
m1 = (fS[li] - fS[li - 1]) / (S[li] - S[li - 1])
b1 = fS[li] - m1 * S[li]
m2 = (fS[li + 2] - fS[li + 1]) / (S[li + 2] - S[li + 1])
b2 = fS[li + 1] - m2 * S[li + 1]
# compute the two lines' intersection
# Make sure it's in the valid range
ix = (b1 - b2) / (m2 - m1)
if not (ix >= S[li] and ix <= S[li + 1]):
# This seems to happen when fS goes from a reasonable to an unreasonable range
# import pdb; pdb.set_trace()
ix = np.clip(ix, S[li] + np.spacing(1), S[li + 1] - np.spacing(1))
# pro = np.exp(b1)/m1 * ( np.exp(m1*ix) - np.exp(m1*S[li]) )
lnpr1 = _signed_lse(m1, b1, ix, S[li])
h = Hull()
h.m = m1
h.b = b1
h.lnpr = lnpr1
h.left = S[li]
h.right = ix
upperHull.append(h)
# pro = np.exp(b2)/m2 * ( np.exp(m2*S[li+1]) - np.exp(m2*ix) )
lnpr2 = _signed_lse(m2, b2, S[li + 1], ix)
h = Hull()
h.m = m2
h.b = b2
h.lnpr = lnpr2
h.left = ix
h.right = S[li + 1]
upperHull.append(h)
# second to last line (m<0)
m = (fS[-2] - fS[-3]) / (S[-2] - S[-3])
b = fS[-2] - m * S[-2]
# pro = np.exp(b)/m * ( np.exp(m*S[-1]) - np.exp(m*S[-2]) )
lnpr = _signed_lse(m, b, S[-1], S[-2])
h = Hull()
h.m = m
h.b = b
h.lnpr = lnpr
h.left = S[-2]
h.right = S[-1]
upperHull.append(h)
if np.isinf(domain[1]):
# last line (to infinity)
m = (fS[-1] - fS[-2]) / (S[-1] - S[-2])
b = fS[-1] - m * S[-1]
# pro = np.exp(b)/m * ( 0 - np.exp(m*S[-1]) )
lnpr = b - np.log(np.abs(m)) + m * S[-1]
h = Hull()
h.m = m
h.b = b
h.lnpr = lnpr
h.left = S[-1]
h.right = np.Inf
upperHull.append(h)
lnprs = np.array([h.lnpr for h in upperHull])
lnZ = logsumexp(lnprs)
prs = np.exp(lnprs - lnZ)
for (i, h) in enumerate(upperHull):
h.pr = prs[i]
if not np.all(np.isfinite(prs)):
raise Exception("ARS prs contains Inf or NaN")
return lowerHull, upperHull
import numpy as np
EPSILON = np.spacing(1)
SPATIAL_DIMENSIONS = 2, 3, 4
class TverskyLoss:
def __init__(self, *, alpha, beta, epsilon=None):
self.alpha = alpha
self.beta = beta
self.epsilon = EPSILON if epsilon is None else epsilon
def __call__(self, output, target):
loss = get_tversky_loss(
output,
target,
self.alpha,
self.beta,
epsilon=self.epsilon,
)
return loss
class DiceLoss(TverskyLoss):
def __init__(self, epsilon=None):
super().__init__(alpha=0.5, beta=0.5, epsilon=epsilon)
def get_confusion(output, target):
if output.shape != target.shape:
message = (
from numpy import spacing, prod, fromfile, exp, log, inf, isfinite
from numpy.linalg import norm, inv
from sys import stdout
from os import remove
from os.path import exists, expanduser, isdir
from time import time
from numpy.random import RandomState
from numpy.lib.stride_tricks import as_strided
from warnings import filterwarnings
import warnings
from Params import Params
from types import IntType, LongType, FloatType, StringType
NumType = (IntType, LongType, FloatType)
# Small number
eps = spacing(1.)
"""
Normalize stimulus
stim: numpy array with sample number as last dimension
pixelNorm: If True, normalize each location by individual mean and stdev.
If a tuple, use first element as mean and second as stdev.
Otherwise, normalize using global statistics.
Returns normalized stimulus, mean, and stdev.
"""
def normStim(stim, pixelNorm=True):
# Ignore divide by zero warnings
filterwarnings('ignore', 'invalid value encountered in divide')
def gnmf(X,
A,
lambd=0,
n_components=None,
tol_nmf=1e-3,
max_iter=100,
verbose=False):
X = check_array(X)
check_non_negative(X, "NMF.fit")
n_samples, n_features = X.shape
if not n_components:
n_components = min(n_samples, n_features)
else:
n_components = n_components
W, H = NBS_init(X, n_components, init='nndsvd')
list_reconstruction_err_ = []
reconstruction_err_ = LA.norm(X - np.dot(W, H))
list_reconstruction_err_.append(reconstruction_err_)
eps = np.spacing(1) # 2.2204460492503131e-16
Lp = np.matrix(np.diag(np.asarray(A).sum(axis=0))) # degree matrix
Lm = A
for n_iter in range(1, max_iter + 1):
if verbose:
print(
"Iteration ={:4d} / {:d} - - - - — Error = {:.2f} - - - - — Tolerance = {:f}"
.format(n_iter, max_iter, reconstruction_err_, tol_nmf))
h1 = lambd * np.dot(H, Lm) + np.dot(W.T,
(X + eps) / (np.dot(W, H) + eps))
h2 = lambd * np.dot(H, Lp) + np.dot(W.T, np.ones(X.shape))
H = np.multiply(H, (h1 + eps) / (h2 + eps))
H[H <= 0] = eps
H[np.isnan(H)] = eps
w1 = np.dot((X + eps) / (np.dot(W, H) + eps), H.T)
w2 = np.dot(np.ones(X.shape), H.T)
W = np.multiply(W, (w1 + eps) / (w2 + eps))
W[W <= 0] = eps
W[np.isnan(W)] = eps
if reconstruction_err_ > LA.norm(X - np.dot(W, H)):
H = (1 - eps) * H + eps * np.random.normal(
0, 1, (n_components, n_features))**2
W = (1 - eps) * W + eps * np.random.normal(
0, 1, (n_samples, n_components))**2
reconstruction_err_ = LA.norm(X - np.dot(W, H))
list_reconstruction_err_.append(reconstruction_err_)
if reconstruction_err_ < tol_nmf:
warnings.warn("Tolerance error reached during fit")
break
if np.isnan(W).any() or np.isnan(H).any():
warnings.warn("NaN values at " + str(n_iter) + " Error=" +
str(reconstruction_err_))
break
if n_iter == max_iter:
warnings.warn("Iteration limit reached during fit")
return (np.squeeze(np.asarray(W)), np.squeeze(np.asarray(H)),
list_reconstruction_err_)
def AngSetupFSC(X, Y, bX, bY, bZ, PA, PB, nu):
"""
AngSetupSurf calculates the displacements associated with an angular
dislocation pair on each side of an RD in a half-space.
"""
SideVec = PB - PA
eZ = numpy.array([0, 0, 1])
beta = numpy.arccos(-SideVec.dot(eZ) / norm(SideVec))
eps = numpy.spacing(
1) # distance between 1 and the nearest floating point number
if numpy.abs(beta) < eps or numpy.abs(numpy.pi - beta) < eps:
ue = numpy.zeros(numpy.shape(X))
un = numpy.zeros(numpy.shape(X))
uv = numpy.zeros(numpy.shape(X))
else:
ey1 = numpy.array([*SideVec[0:2], 0])
ey1 = ey1 / norm(ey1)
ey3 = -eZ
ey2 = numpy.cross(ey3, ey1)
A = numpy.array([ey1, ey2, ey3]) # Transformation matrix
# Transform coordinates from EFCS to the first ADCS
[y1A, y2A, unused] = CoordTrans(X - PA[0], Y - PA[1],
numpy.zeros(X.size) - PA[2], A)
# Transform coordinates from EFCS to the second ADCS
[y1AB, y2AB, unused] = CoordTrans(SideVec[0], SideVec[1], SideVec[2],
A)
y1B = y1A - y1AB
y2B = y2A - y2AB
# Transform slip vector components from EFCS to ADCS
[b1, b2, b3] = CoordTrans(bX, bY, bZ, A)
# Determine the best artefact-free configuration for the calculation
# points near the free surface
I = (beta * y1A) >= 0
J = numpy.logical_not(I)
v1A = numpy.zeros(I.shape)
v2A = numpy.zeros(I.shape)
v3A = numpy.zeros(I.shape)
v1B = numpy.zeros(I.shape)
v2B = numpy.zeros(I.shape)
v3B = numpy.zeros(I.shape)
# Configuration I
v1A[I], v2A[I], v3A[I] = AngDisDispSurf(y1A[I], y2A[I],
-numpy.pi + beta, b1, b2, b3,
nu, -PA[2])
v1B[I], v2B[I], v3B[I] = AngDisDispSurf(y1B[I], y2B[I],
-numpy.pi + beta, b1, b2, b3,
nu, -PB[2])
# Configuration II
v1A[J], v2A[J], v3A[J] = AngDisDispSurf(y1A[J], y2A[J], beta, b1, b2,
b3, nu, -PA[2])
v1B[J], v2B[J], v3B[J] = AngDisDispSurf(y1B[J], y2B[J], beta, b1, b2,
b3, nu, -PB[2])
# Calculate total displacements in ADCS
v1 = v1B - v1A
v2 = v2B - v2A
v3 = v3B - v3A
# Transform total displacements from ADCS to EFCS
[ue, un, uv] = CoordTrans(v1, v2, v3, A.transpose())
return ue, un, uv
def RandomLearning(LPATH, LFILE, LCNT, b, color, idn):
def print2f(MSG):
lf = open(LOGPATH + LFILE + '_out' + idn + '_random.txt', 'a')
print >> lf, MSG
lf.close()
ff = open(LPATH + LFILE, 'r')
idx = 0
fRNA = np.zeros((LCNT, 23 * 4))
label = np.zeros((LCNT, ))
for line in ff:
f = line.split('\t')
if (int(f[1]) == 1):
label[idx] = 1
else:
label[idx] = -1
fRNA[idx] = Ronehot(f[0])
idx += 1
X_train, X_test, y_train, y_test = train_test_split(fRNA,
label,
test_size=0.2,
random_state=0)
print2f((np.shape(X_train), np.shape(y_train), np.shape(X_test),
np.shape(y_test)))
TRAINLEN = np.shape(X_train)[0]
INTLEN = int(TRAINLEN * 0.01)
aa = np.split(X_train, [TRAINLEN - INTLEN, TRAINLEN - INTLEN])
X_train = aa[0]
inttrain_x = aa[2]
aa = np.split(y_train, [TRAINLEN - INTLEN, TRAINLEN - INTLEN])
y_train = aa[0]
inttrain_y = aa[2]
TRAINLEN = np.shape(X_train)[0]
INTLEN = np.shape(inttrain_x)[0]
AUGLEN = TRAINLEN * 16
Uset = set()
Lset = set()
for i in range(0, INTLEN):
Lset.add(i)
for i in range(INTLEN, TRAINLEN):
Uset.add(i)
train_x = np.zeros((AUGLEN, 23 * 4))
train_y = np.zeros((AUGLEN, ))
train_l = np.zeros((AUGLEN, ))
patch = [set() for x in range(0, TRAINLEN)]
for i in range(0, TRAINLEN):
sample = X_train[i]
R1 = np.zeros((4))
R2 = np.zeros((4))
for j in range(0, 4):
R1[j] = 1
for k in range(0, 4):
R2[k] = 1
RR = np.concatenate((R1, R2))
for x in range(0, 8):
sample[x] = RR[x]
train_x[i * 16 + j * 4 + k] = sample
train_y[i * 16 + j * 4 + k] = y_train[i]
train_l[i * 16 + j * 4 + k] = i
patch[i].add(i * 16 + j * 4 + k)
R2[k] = 0
R1[j] = 0
print2f((TRAINLEN, AUGLEN, INTLEN))
print2f(
(np.shape(X_train)[0], np.shape(train_x)[0], np.shape(inttrain_x)[0]))
print2f((patch[0]))
clf = GradientBoostingClassifier().fit(inttrain_x, inttrain_y)
print2f(("init: ", clf.score(X_test, y_test)))
clf2 = GradientBoostingClassifier().fit(X_train, y_train)
print2f(("complete: ", clf2.score(X_test, y_test)))
#for i in range(10,20):
# print (clf.predict(X_test[i]), clf.predict_proba(X_test[i])[0][1], clf.predict_log_proba(X_test[i])[0][1], math.log(clf.predict_proba(X_test[i])[0][1]), y_test[i])
eps = np.spacing(1)
ITER = int(TRAINLEN / b)
patchsize = 16
predpatch = [0.0 for x in range(0, patchsize)]
ACC = []
ITR = []
LAB = []
for IT in range(0, ITER):
if (INTLEN + b > TRAINLEN):
print2f(("OUT OF RANGE "))
break
Rm = random.sample(Uset, b)
for elm in Rm:
Lset.add(elm)
Uset.remove(elm)
inttrain_x = np.concatenate((inttrain_x, [X_train[elm]]), axis=0)
inttrain_y = np.concatenate((inttrain_y, [y_train[elm]]), axis=0)
INTLEN += 1
print2f((np.shape(inttrain_x)[0], len(Lset), len(Uset)))
clf = GradientBoostingClassifier().fit(inttrain_x, inttrain_y)
res = clf.score(X_test, y_test)
print2f(("iter: ", IT, res))
ACC.append(res)
ITR.append(IT)
LAB.append(len(Lset))
plt.plot(LAB, ACC, color)
#plt.plot(LAB,ACC,'b*')
plt.xlabel('Num of labels')
plt.ylabel('Accuracy')
plt.ylim(0.5, 1.0)
plt.title(LFILE)
plt.show()
def _valid_epoch(self, epoch):
if self.val_loader is None:
if self.gpu == 0:
self.logger.warning(
'Not data loader was passed for the validation step, No validation is performed !'
)
return {}
if self.gpu == 0:
self.logger.info('\n###### EVALUATION ######')
self.model.eval()
self.wrt_mode = 'val'
total_loss_val = AverageMeter()
total_inter, total_union = 0, 0
total_correct, total_label = 0, 0
tbar = tqdm(self.val_loader, ncols=130)
with torch.no_grad():
if self.gpu == 0:
val_visual = []
for batch_idx, (data, target) in enumerate(tbar):
target, data = target.cuda(non_blocking=True), data.cuda(
non_blocking=True)
H, W = target.size(1), target.size(2)
up_sizes = (ceil(H / 8) * 8, ceil(W / 8) * 8)
pad_h, pad_w = up_sizes[0] - data.size(
2), up_sizes[1] - data.size(3)
data = F.pad(data, pad=(0, pad_w, 0, pad_h), mode='reflect')
output = self.model(data)
output = output[:, :, :H, :W]
# LOSS
loss = F.cross_entropy(output,
target,
ignore_index=self.ignore_index)
total_loss_val.update(loss.item())
# eval_metrics has already implemented DDP synchronized
correct, labeled, inter, union = eval_metrics(
output, target, self.num_classes, self.ignore_index)
total_inter, total_union = total_inter + inter, total_union + union
total_correct, total_label = total_correct + correct, total_label + labeled
if self.gpu == 0:
# LIST OF IMAGE TO VIZ (15 images)
if len(val_visual) < 15:
if isinstance(data, list): data = data[0]
target_np = target.data.cpu().numpy()
output_np = output.data.max(1)[1].cpu().numpy()
val_visual.append(
[data[0].data.cpu(), target_np[0], output_np[0]])
# PRINT INFO
pixAcc = 1.0 * total_correct / (np.spacing(1) + total_label)
IoU = 1.0 * total_inter / (np.spacing(1) + total_union)
mIoU = IoU.mean()
seg_metrics = {
"Pixel_Accuracy":
np.round(pixAcc, 3),
"Mean_IoU":
np.round(mIoU, 3),
"Class_IoU":
dict(zip(range(self.num_classes), np.round(IoU, 3)))
}
if self.gpu == 0:
tbar.set_description(
'EVAL ({}) | Loss: {:.3f}, PixelAcc: {:.2f}, Mean IoU: {:.2f} |'
.format(epoch, total_loss_val.average, pixAcc, mIoU))
if self.gpu == 0:
self._add_img_tb(val_visual, 'val')
# METRICS TO TENSORBOARD
self.wrt_step = (epoch) * len(self.val_loader)
self.writer.add_scalar(f'{self.wrt_mode}/loss',
total_loss_val.average, self.wrt_step)
for k, v in list(seg_metrics.items())[:-1]:
self.writer.add_scalar(f'{self.wrt_mode}/{k}', v,
self.wrt_step)
log = {'val_loss': total_loss_val.average, **seg_metrics}
return log
def accumulate(self, p=None):
'''
Accumulate per image evaluation results and store the result in self.eval
:param p: input params for evaluation
:return: None
'''
print('Accumulating evaluation results...')
tic = time.time()
if not self.evalImgs:
print('Please run evaluate() first')
# allows input customized parameters
if p is None:
p = self.params
p.catIds = p.catIds if p.useCats == 1 else [-1]
T = len(p.iouThrs)
R = len(p.recThrs)
K = len(p.catIds) if p.useCats else 1
A = len(p.areaRng)
M = len(p.maxDets)
precision = -np.ones(
(T, R, K, A, M)) # -1 for the precision of absent categories
recall = -np.ones((T, K, A, M))
scores = -np.ones((T, R, K, A, M))
# create dictionary for future indexing
_pe = self._paramsEval
catIds = _pe.catIds if _pe.useCats else [-1]
setK = set(catIds)
setA = set(map(tuple, _pe.areaRng))
setM = set(_pe.maxDets)
setI = set(_pe.imgIds)
# get inds to evaluate
k_list = [n for n, k in enumerate(p.catIds) if k in setK]
m_list = [m for n, m in enumerate(p.maxDets) if m in setM]
a_list = [
n for n, a in enumerate(map(lambda x: tuple(x), p.areaRng))
if a in setA
]
i_list = [n for n, i in enumerate(p.imgIds) if i in setI]
I0 = len(_pe.imgIds)
A0 = len(_pe.areaRng)
# retrieve E at each category, area range, and max number of detections
for k, k0 in enumerate(k_list):
Nk = k0 * A0 * I0
for a, a0 in enumerate(a_list):
Na = a0 * I0
for m, maxDet in enumerate(m_list):
E = [self.evalImgs[Nk + Na + i] for i in i_list]
E = [e for e in E if not e is None]
if len(E) == 0:
continue
dtScores = np.concatenate(
[e['dtScores'][0:maxDet] for e in E])
# different sorting method generates slightly different results.
# mergesort is used to be consistent as Matlab implementation.
inds = np.argsort(-dtScores, kind='mergesort')
dtScoresSorted = dtScores[inds]
dtm = np.concatenate(
[e['dtMatches'][:, 0:maxDet] for e in E], axis=1)[:,
inds]
dtIg = np.concatenate(
[e['dtIgnore'][:, 0:maxDet] for e in E], axis=1)[:,
inds]
gtIg = np.concatenate([e['gtIgnore'] for e in E])
npig = np.count_nonzero(gtIg == 0)
if npig == 0:
continue
tps = np.logical_and(dtm, np.logical_not(dtIg))
fps = np.logical_and(np.logical_not(dtm),
np.logical_not(dtIg))
tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float)
fp_sum = np.cumsum(fps, axis=1).astype(dtype=np.float)
for t, (tp, fp) in enumerate(zip(tp_sum, fp_sum)):
tp = np.array(tp)
fp = np.array(fp)
nd = len(tp)
rc = tp / npig
pr = tp / (fp + tp + np.spacing(1))
q = np.zeros((R, ))
ss = np.zeros((R, ))
if nd:
recall[t, k, a, m] = rc[-1]
else:
recall[t, k, a, m] = 0
# numpy is slow without cython optimization for accessing elements
# use python array gets significant speed improvement
pr = pr.tolist()
q = q.tolist()
#To get a zig-zag PR curve - comment the loop below
#for i in range(nd-1, 0, -1):
# if pr[i] > pr[i-1]:
# pr[i-1] = pr[i]
inds = np.searchsorted(rc, p.recThrs, side='left')
try:
for ri, pi in enumerate(inds):
q[ri] = pr[pi]
ss[ri] = dtScoresSorted[pi]
except:
pass
precision[t, :, k, a, m] = np.array(q)
scores[t, :, k, a, m] = np.array(ss)
self.eval = {
'params': p,
'counts': [T, R, K, A, M],
'date': datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
'precision': precision,
'recall': recall,
'scores': scores,
}
toc = time.time()
print('DONE (t={:0.2f}s).'.format(toc - tic))
def weighted_average_slopes(data, old_start, old_dt, new_start, new_dt,
new_npts, *args, **kwargs):
r"""
Implements the weighted average slopes interpolation scheme proposed in
[Wiggins1976]_ for evenly sampled data. The scheme guarantees that there
will be no additional extrema after the interpolation in contrast to
spline interpolation.
The slope :math:`s_i` at each knot is given by a weighted average of the
adjacent linear slopes :math:`m_i` and :math:`m_{i+j}`:
.. math::
s_i = (w_i m_i + w_{i+1} m_{i+1}) / (w_i + w_{i+1})
where
.. math::
w = 1 / max \left\{ \left\| m_i \\right\|, \epsilon \\right\}
The value at each data point and the slope are then plugged into a
piecewise continuous cubic polynomial used to evaluate the interpolated
sample points.
:type data: array_like
:param data: Array to interpolate.
:type old_start: float
:param old_start: The start of the array as a number.
:type old_start: float
:param old_dt: The time delta of the current array.
:type new_start: float
:param new_start: The start of the interpolated array. Must be greater
or equal to the current start of the array.
:type new_dt: float
:param new_dt: The desired new time delta.
:type new_npts: int
:param new_npts: The new number of samples.
"""
old_end, new_end = _validate_parameters(data, old_start, old_dt,
new_start, new_dt, new_npts)
# In almost all cases the unit will be in time.
new_time_array = np.linspace(new_start, new_end, new_npts)
m = np.diff(data) / old_dt
w = np.abs(m)
w = 1.0 / np.clip(w, np.spacing(1), w.max())
slope = np.empty(len(data), dtype=np.float64)
slope[0] = m[0]
slope[1:-1] = (w[:-1] * m[:-1] + w[1:] * m[1:]) / (w[:-1] + w[1:])
slope[-1] = m[-1]
# If m_i and m_{i+1} have opposite signs then set the slope to zero.
# This forces the curve to have extrema at the sample points and not
# in-between.
sign_change = np.diff(np.sign(m)).astype(np.bool)
slope[1:-1][sign_change] = 0.0
derivatives = np.empty((len(data), 2), dtype=np.float64)
derivatives[:, 0] = data
derivatives[:, 1] = slope
# Create interpolated value using hermite interpolation. In this case
# it is directly applicable as the first derivatives are known.
# Using scipy.interpolate.piecewise_polynomial_interpolate() is too
# memory intensive
return_data = np.empty(len(new_time_array), dtype=np.float64)
clibsignal.hermite_interpolation(data, slope, new_time_array, return_data,
len(data), len(return_data), old_dt,
old_start)
return return_data
predict(i, future, trainlen)
print("--- %s seconds ---" % (time.time() - start_time))
rc("text", usetex=False)
plt.figure(figsize=(16, 8))
plt.plot(
range(0, trainlen + futureTotal),
y[1000:trainlen + futureTotal],
"b",
label="Data",
alpha=0.3,
)
# plt.plot(range(0,trainlen),pred_training,'.g', alpha=0.3)
plt.plot(
range(trainlen, trainlen + futureTotal),
predictedTotal,
"k",
alpha=0.8,
label="Free Running ESN",
)
lo, hi = plt.ylim()
plt.plot([trainlen, trainlen], [lo + np.spacing(1), hi - np.spacing(1)],
"k:",
linewidth=4)
plt.title(r"Ground Truth and Echo State Network Output", fontsize=25)
plt.xlabel(r"Time (Days)", fontsize=20, labelpad=10)
plt.ylabel(r"Price ($)", fontsize=20, labelpad=10)
plt.legend(fontsize="xx-large", loc="best")
plt.savefig("/home/homeuser/Stonks/ESN.png")
def test_half_fpe(self):
with np.errstate(all='raise'):
sx16 = np.array((1e-4, ), dtype=float16)
bx16 = np.array((1e4, ), dtype=float16)
sy16 = float16(1e-4)
by16 = float16(1e4)
# Underflow errors
assert_raises_fpe('underflow', lambda a, b: a * b, sx16, sx16)
assert_raises_fpe('underflow', lambda a, b: a * b, sx16, sy16)
assert_raises_fpe('underflow', lambda a, b: a * b, sy16, sx16)
assert_raises_fpe('underflow', lambda a, b: a * b, sy16, sy16)
assert_raises_fpe('underflow', lambda a, b: a / b, sx16, bx16)
assert_raises_fpe('underflow', lambda a, b: a / b, sx16, by16)
assert_raises_fpe('underflow', lambda a, b: a / b, sy16, bx16)
assert_raises_fpe('underflow', lambda a, b: a / b, sy16, by16)
assert_raises_fpe('underflow', lambda a, b: a / b,
float16(2.**-14), float16(2**11))
assert_raises_fpe('underflow', lambda a, b: a / b,
float16(-2.**-14), float16(2**11))
assert_raises_fpe('underflow', lambda a, b: a / b,
float16(2.**-14 + 2**-24), float16(2))
assert_raises_fpe('underflow', lambda a, b: a / b,
float16(-2.**-14 - 2**-24), float16(2))
assert_raises_fpe('underflow', lambda a, b: a / b,
float16(2.**-14 + 2**-23), float16(4))
# Overflow errors
assert_raises_fpe('overflow', lambda a, b: a * b, bx16, bx16)
assert_raises_fpe('overflow', lambda a, b: a * b, bx16, by16)
assert_raises_fpe('overflow', lambda a, b: a * b, by16, bx16)
assert_raises_fpe('overflow', lambda a, b: a * b, by16, by16)
assert_raises_fpe('overflow', lambda a, b: a / b, bx16, sx16)
assert_raises_fpe('overflow', lambda a, b: a / b, bx16, sy16)
assert_raises_fpe('overflow', lambda a, b: a / b, by16, sx16)
assert_raises_fpe('overflow', lambda a, b: a / b, by16, sy16)
assert_raises_fpe('overflow', lambda a, b: a + b, float16(65504),
float16(17))
assert_raises_fpe('overflow', lambda a, b: a - b, float16(-65504),
float16(17))
assert_raises_fpe('overflow', np.nextafter, float16(65504),
float16(np.inf))
assert_raises_fpe('overflow', np.nextafter, float16(-65504),
float16(-np.inf))
assert_raises_fpe('overflow', np.spacing, float16(65504))
# Invalid value errors
assert_raises_fpe('invalid', np.divide, float16(np.inf),
float16(np.inf))
assert_raises_fpe('invalid', np.spacing, float16(np.inf))
assert_raises_fpe('invalid', np.spacing, float16(np.nan))
assert_raises_fpe('invalid', np.nextafter, float16(np.inf),
float16(0))
assert_raises_fpe('invalid', np.nextafter, float16(-np.inf),
float16(0))
assert_raises_fpe('invalid', np.nextafter, float16(0),
float16(np.nan))
# These should not raise
float16(65472) + float16(32)
float16(2**-13) / float16(2)
float16(2**-14) / float16(2**10)
np.spacing(float16(-65504))
np.nextafter(float16(65504), float16(-np.inf))
np.nextafter(float16(-65504), float16(np.inf))
float16(2**-14) / float16(2**10)
float16(-2**-14) / float16(2**10)
float16(2**-14 + 2**-23) / float16(2)
float16(-2**-14 - 2**-23) / float16(2)
def getThresh(pks, doClip, pnorm=0.5):
""" Function for deciding threshold for peaks given heights of all peaks.
Args:
pks: 1-D array
height of all peaks
doClip: int
pnorm: float, between 0 and 1, default is 0.5
a variable deciding the amount of spikes chosen
Returns:
thresh: float
threshold for choosing as spikes or not
"""
spread = np.array([pks.min(), pks.max()])
spread = spread + np.diff(spread) * np.array([-0.05, 0.05])
low_spk = False
pts = np.linspace(spread[0], spread[1], 2001)
KD = sm.nonparametric.KDEUnivariate(pks)
KD.fit(bw='scott')
f = KD.evaluate(pts)
xi = pts
center = np.where(xi>np.median(pks))[0][0]
fmodel = np.concatenate([f[0:center+1], np.flipud(f[0:center])])
if len(fmodel) < len(f):
fmodel = np.append(fmodel, np.ones(len(f)-len(fmodel))*min(fmodel))
else:
fmodel = fmodel[0:len(f)]
# adjust the model so it doesn't exceed the data:
csf = np.cumsum(f) / np.sum(f)
csmodel = np.cumsum(fmodel) / np.max([np.sum(f), np.sum(fmodel)])
lastpt = np.where(np.logical_and(csf[0:-1]>csmodel[0:-1]+np.spacing(1), csf[1:]<csmodel[1:]))[0]
if not lastpt.size:
lastpt = center
else:
lastpt = lastpt[0]
fmodel[0:lastpt+1] = f[0:lastpt+1]
fmodel[lastpt:] = np.minimum(fmodel[lastpt:],f[lastpt:])
csf = np.cumsum(f)
csmodel = np.cumsum(fmodel)
csf2 = csf[-1] - csf
csmodel2 = csmodel[-1] - csmodel
obj = csf2 ** pnorm - csmodel2 ** pnorm
maxind = np.argmax(obj)
thresh = xi[maxind]
if np.sum(pks>thresh)<30:
low_spk = True
print('Very few spikes were detected at the desired sensitivity/specificity tradeoff. Adjusting threshold to take 30 largest spikes')
thresh = np.percentile(pks, 100*(1-30/len(pks)))
elif ((np.sum(pks>thresh)>doClip) & (doClip>0)):
print('Selecting top',doClip,'spikes for template')
thresh = np.percentile(pks, 100*(1-doClip/len(pks)))
ix = np.argmin(np.abs(xi-thresh))
falsePosRate = csmodel2[ix]/csf2[ix]
detectionRate = (csf2[ix]-csmodel2[ix])/np.max(csf2-csmodel2)
return thresh, falsePosRate, detectionRate, low_spk
def maxent(A, b, lamb, w=None, x0=None):
assert isinstance(
A, ndarray) and A.ndim == 2, 'A is expected to be a 2-D Numpy array'
assert isinstance(b, ndarray), 'b is expected to be a 1-D Numpy array'
b = b.squeeze()
assert b.ndim <= 1, 'b is expected to be a 1-D Numpy array'
lamb = atleast_1d(lamb)
assert lamb.ndim == 1 and lamb.size >= 0, 'lamb must be a 1-D vector or scalar'
assert (lamb >= 0).all(), 'Regularization parameter lamb must be positive'
#%% Set defaults.
flat = 1e-3 # Measures a flat minimum.
flatrange = 10 # How many iterations before a minimum is considered flat.
maxit = 150 # Maximum number of CG iterations.
minstep = 1e-12 # Determines the accuracy of x_lambda.
sigma = 0.5 # Threshold used in descent test.
tau0 = 1e-3 # Initial threshold used in secant root finder.
#%% Initialization.
m, n = A.shape
lamb = atleast_1d(lamb)
Nlambda = lamb.size
x_lambda = zeros((n, Nlambda), order='F')
F = zeros(maxit)
if w is None:
w = ones(n, dtype=float) #needs to be column vector
if x0 is None:
x0 = ones(n, dtype=float) #needs to be column vector
rho = empty(Nlambda, dtype=float)
eta = empty(Nlambda, dtype=float)
# Treat each lambda separately.
for j in range(Nlambda):
# Prepare for nonlinear CG iteration.
l2 = lamb[j]**2.
x = x0
Ax = A.dot(x)
g = 2. * A.T.dot(Ax - b) + l2 * (1 + log(w * x))
p = -g
r = Ax - b
# Start the nonlinear CG iteration here.
delta_x = x
dF = 1
it = 0
phi0 = p.T.dot(g)
data = zeros((maxit, 3), dtype=float, order='F')
X = zeros((n, maxit), dtype=float, order='F')
while (norm(delta_x, 2) > minstep * norm(x, 2) and dF > flat
and it < maxit and phi0 < 0):
# Compute some CG quantities.
Ap = A.dot(p)
gamma = Ap.T.dot(Ap)
v = A.T.dot(Ap)
# Determine the steplength alpha by "soft" line search in which
# the minimum of phi(alpha) = p'*g(x + alpha*p) is determined to
# a certain "soft" tolerance.
# First compute initial parameters for the root finder.
alpha_left = 0
phi_left = phi0
if p.min() >= 0:
alpha_right = -phi0 / (2 * gamma)
h = 1. + alpha_right * p / x
else:
# Step-length control to insure a positive x + alpha*p.
I = where(p < 0)[0]
alpha_right = (-x[I] / p[I]).min()
h = 1. + alpha_right * p / x
delta = spacing(1) #replacement for matlab eps
while h.min() <= 0:
alpha_right = alpha_right * (1 - delta)
h = 1 + alpha_right * p / x
delta = delta * 2
z = log(h)
phi_right = phi0 + 2 * alpha_right * gamma + l2 * p.T.dot(z)
alpha = alpha_right
phi = phi_right
if phi_right <= 0: # Special treatment of the case when phi(alpha_right) = 0.
z = log(1 + alpha * p / x)
g_new = g + l2 * z + 2 * alpha * v
t = g_new.T.dot(g_new)
beta = (t - g.T.dot(g_new)) / (phi - phi0)
else:
# The regular case: improve the steplength alpha iteratively
# until the new step is a descent step.
t = 1
u = 1
tau = tau0
uit = 0
while u > -sigma * t:
uold = u
# Use the secant method to improve the root of phi(alpha) = 0
# to within an accuracy determined by tau.
phiit = 0
while absolute(phi / phi0) > tau:
phiold = phi
alphaold = alpha
alpha = (alpha_left * phi_right - alpha_right *
phi_left) / (phi_right - phi_left)
z = log(1 + alpha * p / x)
phi = phi0 + 2 * alpha * gamma + l2 * p.T.dot(z)
if phiold == phi and alphaold == alpha and phiit > maxit:
warn('secant is not converging: abs(phi/phi0) = ' +
str(absolute(phi / phi0)) +
' terminating phi search on iteration ' +
str(phiit))
break
if phi > 0:
alpha_right = alpha
phi_right = phi
else:
alpha_left = alpha
phi_left = phi
phiit += 1
# To check the descent step, compute u = p'*g_new and
# t = norm(g_new)^2, where g_new is the gradient at x + alpha*p.
g_new = g + l2 * z + 2 * alpha * v
t = g_new.T.dot(g_new)
beta = (t - g.T.dot(g_new)) / (phi - phi0)
u = -t + beta * phi
if u == uold and uit > maxit:
warn(
'excessive descent iterations, terminating search on iteration '
+ str(phiit))
break
tau = tau / 10.
uit += 1
# Update the iteration vectors.
g = g_new
delta_x = alpha * p
x = x + delta_x
p = -g + beta * p
r = r + alpha * Ap
phi0 = p.T.dot(g)
# Compute some norms and check for flat minimum.
rho[j] = norm(r)
eta[j] = x.T.dot(log(w * x))
F[it] = rho[j]**2 + l2 * eta[j]
if it <= flatrange:
dF = 1.
else:
dF = absolute(F[it] - F[it - flatrange]) / absolute(F[it])
data[it, ...] = array([F[it], norm(delta_x), norm(g)])
X[..., it] = x
it += 1
x_lambda[..., j] = x
return x_lambda.squeeze(), rho, eta
def prepare_data(mode, train_or_test):
'''
:param
mode: type str, 'global' or 'once' ， global用来获取全局的spk_to_idx的字典，所有说话人的列表等等
train_or_test:type str, 'train','valid' or 'test'
其中把每个文件夹每个人的按文件名的排序的前70%作为训练，70-80%作为valid，最后20%作为测试
:return:
'''
mix_speechs = np.zeros((config.BATCH_SIZE, config.MAX_LEN))
mix_feas = [] #应该是bs,n_frames,n_fre这么多
mix_phase = [] #应该是bs,n_frames,n_fre这么多
aim_fea = [] #应该是bs,n_frames,n_fre这么多
aim_spkid = [] #np.zeros(config.BATCH_SIZE)
aim_spkname = [] #np.zeros(config.BATCH_SIZE)
query = [] #应该是BATCH_SIZE，shape(query)的形式，用list再转换把
multi_spk_fea_list = [] #应该是bs个dict，每个dict里是说话人name为key，clean_fea为value的字典
multi_spk_wav_list = [] #应该是bs个dict，每个dict里是说话人name为key，clean_fea为value的字典
#目标数据集的总data，底下应该存放分目录的文件夹，每个文件夹应该名字是sX
data_path = config.aim_path + '/data'
#语音刺激
if config.MODE == 1:
if config.DATASET == 'WSJ0': #开始构建数据集
WSJ0_eval_list = [
'440', '441', '442', '443', '444', '445', '446', '447'
]
WSJ0_test_list = [
'22g', '22h', '050', '051', '052', '053', '420', '421', '422',
'423'
]
all_spk_train = os.listdir(data_path + '/train')
all_spk_eval = os.listdir(data_path + '/eval')
all_spk_test = os.listdir(data_path + '/test')
all_spk_evaltest = os.listdir(data_path + '/evaL_test')
all_spk = all_spk_train + all_spk_eval + all_spk_test
spk_samples_list = {}
batch_idx = 0
mix_k = random.randint(config.MIN_MIX, config.MAX_MIX)
while True:
mix_len = 0
# mix_k=random.randint(config.MIN_MIX,config.MAX_MIX)
if train_or_test == 'train':
aim_spk_k = random.sample(all_spk_train, mix_k) #本次混合的候选人
elif train_or_test == 'eval':
aim_spk_k = random.sample(all_spk_eval, mix_k) #本次混合的候选人
elif train_or_test == 'test':
aim_spk_k = random.sample(all_spk_test, mix_k) #本次混合的候选人
elif train_or_test == 'eval_test':
aim_spk_k = random.sample(all_spk_evaltest,
mix_k) #本次混合的候选人
multi_fea_dict_this_sample = {}
multi_wav_dict_this_sample = {}
channel_act = None
if 1 and config.dB and mix_k == 2:
dB_rate = 10**(config.dB / 20.0 * np.random.rand()
) #10**(0——0.25)
if np.random.rand() > 0.5: #选择哪个通道来
channel_act = 1
else:
channel_act = 2
# print 'channel to change with dB:',channel_act,dB_rate
if 1 and config.dB and config.MAX_MIX == 3 and mix_k == 3:
'DB with 3 channels.'
dB_rate_large = 10**(config.dB / 20.0 *
(0.5 + 0.5 * np.random.rand())
) #10**(0.125——0.25)
dB_rate_small = 10**(config.dB / 20.0 *
(0.5 * np.random.rand())
) #10**(0--0.125)
dB_rate_normal = 10**(config.dB / 20.0 * 0.5
) #10**(0--0.125)
channel_act = 'ALL'
# rrr=np.random.rand()
# if rrr>2/3.0: #选择哪个通道来
# channel_act=1
# elif rrr<1/3.0:
# channel_act=2
# else:
# channel_act==3
# print 'channel to change with dB:',channel_act,dB_rate
for k, spk in enumerate(aim_spk_k):
#选择dB的通道～！
#若是没有出现在整体列表内就注册进去,且第一次的时候读取所有的samples的名字
if spk not in spk_samples_list:
spk_samples_list[spk] = []
for ss in os.listdir(data_path + '/' + train_or_test +
'/' + spk):
spk_samples_list[spk].append(ss[:-4]) #去掉.wav后缀
if 0: #这部分是选择独立同分布的
#这个函数让spk_sanmples_list[spk]按照设定好的方式选择是train的部分还是test
spk_samples_list[spk] = split_forTrainDevTest(
spk_samples_list[spk], train_or_test)
#这个时候这个spk已经注册了，所以直接从里面选就好了
sample_name = random.sample(spk_samples_list[spk], 1)[0]
spk_samples_list[spk].remove(
sample_name) #取出来一次之后，就把这个人去掉（避免一个batch里某段语音的重复出现）
spk_speech_path = data_path + '/' + train_or_test + '/' + spk + '/' + sample_name + '.wav'
signal, rate = sf.read(
spk_speech_path) # signal 是采样值，rate 是采样频率
if len(signal.shape) > 1:
signal = signal[:, 0]
if rate != config.FRAME_RATE:
# 如果频率不是设定的频率则需要进行转换
signal = resampy.resample(signal,
rate,
config.FRAME_RATE,
filter='kaiser_best')
if signal.shape[0] > config.MAX_LEN: # 根据最大长度裁剪
signal = signal[:config.MAX_LEN]
# 更新混叠语音长度
if signal.shape[0] > mix_len:
mix_len = signal.shape[0]
signal -= np.mean(signal) # 语音信号预处理，先减去均值
signal /= np.max(np.abs(signal)) # 波形幅值预处理，幅值归一化
# 如果需要augment数据的话，先进行随机shift, 以后考虑固定shift
if config.AUGMENT_DATA:
random_shift = random.sample(range(len(signal)), 1)[0]
signal = signal[random_shift:] + signal[:random_shift]
if signal.shape[0] < config.MAX_LEN: # 根据最大长度用 0 补齐,
signal = np.append(
signal, np.zeros(config.MAX_LEN - signal.shape[0]))
if k == 0: #第一个作为目标
aim_spkname.append(aim_spk_k[0])
# aim_spk=eval(re.findall('\d+',aim_spk_k[0])[0])-1 #选定第一个作为目标说话人
#TODO:这里有个问题是spk是从１开始的貌似，这个后面要统一一下 --> 已经解决，构建了spk和idx的双向索引
aim_spk_speech = signal
aim_spkid.append(aim_spkname)
if mix_k == 2 and channel_act == 1:
signal = signal * dB_rate
print 'channel 1 for 2db:', dB_rate
if mix_k == 3:
signal = signal * dB_rate_normal
print 'channel 1(normal):', dB_rate_normal
wav_mix = signal
aim_fea_clean = np.transpose(
np.abs(
librosa.core.spectrum.stft(
signal, config.FRAME_LENGTH,
config.FRAME_SHIFT)))
aim_fea.append(aim_fea_clean)
# 把第一个人顺便也注册进去混合dict里
multi_fea_dict_this_sample[spk] = aim_fea_clean
multi_wav_dict_this_sample[spk] = signal
#视频处理部分，为了得到query
else:
if mix_k == 2 and channel_act == 2:
signal = signal * dB_rate
print 'channel 2 for 2db:', dB_rate
if mix_k == 3 and k == 1:
signal = signal * dB_rate_large
print 'channel 2(large):', dB_rate_large
if mix_k == 3 and k == 2:
signal = signal * dB_rate_small
print 'channel 3(small):', dB_rate_small
wav_mix = wav_mix + signal # 混叠后的语音
# 这个说话人的语音
some_fea_clean = np.transpose(
np.abs(
librosa.core.spectrum.stft(
signal,
config.FRAME_LENGTH,
config.FRAME_SHIFT,
)))
multi_fea_dict_this_sample[spk] = some_fea_clean
multi_wav_dict_this_sample[spk] = signal
multi_spk_fea_list.append(
multi_fea_dict_this_sample) #把这个sample的dict传进去
multi_spk_wav_list.append(
multi_wav_dict_this_sample) #把这个sample的dict传进去
# 这里采用log 以后可以考虑采用MFCC或GFCC特征做为输入
if config.IS_LOG_SPECTRAL:
feature_mix = np.log(
np.transpose(
np.abs(
librosa.core.spectrum.stft(
wav_mix,
config.FRAME_LENGTH,
config.FRAME_SHIFT,
window=config.WINDOWS))) + np.spacing(1))
else:
feature_mix = np.transpose(
np.abs(
librosa.core.spectrum.stft(
wav_mix,
config.FRAME_LENGTH,
config.FRAME_SHIFT,
)))
mix_speechs[batch_idx, :] = wav_mix
mix_feas.append(feature_mix)
mix_phase.append(
np.transpose(
librosa.core.spectrum.stft(
wav_mix,
config.FRAME_LENGTH,
config.FRAME_SHIFT,
)))
batch_idx += 1
print 'batch_dix:{}/{}'.format(batch_idx, config.BATCH_SIZE)
if batch_idx == config.BATCH_SIZE: #填满了一个batch
mix_feas = np.array(mix_feas)
mix_phase = np.array(mix_phase)
aim_fea = np.array(aim_fea)
# aim_spkid=np.array(aim_spkid)
query = np.array(query)
print '\nspk_list_from_this_gen:{}'.format(aim_spkname)
print 'aim spk list:', [
one.keys() for one in multi_spk_fea_list
]
# print '\nmix_speechs.shape,mix_feas.shape,aim_fea.shape,aim_spkname.shape,query.shape,all_spk_num:'
# print mix_speechs.shape,mix_feas.shape,aim_fea.shape,len(aim_spkname),query.shape,len(all_spk)
if mode == 'global':
all_spk = sorted(all_spk)
all_spk = sorted(all_spk_train)
all_spk_eval = sorted(all_spk_eval)
all_spk_test = sorted(all_spk_test)
dict_spk_to_idx = {
spk: idx
for idx, spk in enumerate(all_spk)
}
dict_idx_to_spk = {
idx: spk
for idx, spk in enumerate(all_spk)
}
yield all_spk,dict_spk_to_idx,dict_idx_to_spk,\
aim_fea.shape[1],aim_fea.shape[2],32,len(all_spk)
#上面的是：语音长度、语音频率、视频分割多少帧 TODO:后面把这个替换了query.shape[1]
elif mode == 'once':
yield {
'mix_wav': mix_speechs,
'mix_feas': mix_feas,
'mix_phase': mix_phase,
'aim_fea': aim_fea,
'aim_spkname': aim_spkname,
'query': query,
'num_all_spk': len(all_spk),
'multi_spk_fea_list': multi_spk_fea_list,
'multi_spk_wav_list': multi_spk_wav_list
}
batch_idx = 0
mix_speechs = np.zeros((config.BATCH_SIZE, config.MAX_LEN))
mix_feas = [] #应该是bs,n_frames,n_fre这么多
mix_phase = []
aim_fea = [] #应该是bs,n_frames,n_fre这么多
aim_spkid = [] #np.zeros(config.BATCH_SIZE)
aim_spkname = []
query = [] #应该是BATCH_SIZE，shape(query)的形式，用list再转换把
multi_spk_fea_list = []
multi_spk_wav_list = []
else:
raise ValueError('No such dataset:{} for Speech.'.format(
config.DATASET))
pass
#图像刺激
elif config.MODE == 2:
pass
#视频刺激
elif config.MODE == 3:
if config.DATASET == 'AVA':
pass
elif config.DATASET == 'GRID':
#开始构建ＧＲＩＤ数据集
all_spk = os.listdir(data_path)
spk_samples_list = {}
batch_idx = 0
while True:
mix_len = 0
mix_k = random.randint(config.MIN_MIX, config.MAX_MIX)
aim_spk_k = random.sample(all_spk, mix_k) #本次混合的候选人
multi_fea_dict_this_sample = {}
multi_wav_dict_this_sample = {}
for k, spk in enumerate(aim_spk_k):
#若是没有出现在整体列表内就注册进去,且第一次的时候读取所有的samples的名字
if spk not in spk_samples_list:
spk_samples_list[spk] = []
for ss in os.listdir(data_path + '/' + spk + '/' +
spk + '_speech'):
spk_samples_list[spk].append(ss[:-4]) #去掉.wav后缀
#这个函数让spk_sanmples_list[spk]按照设定好的方式选择是train的部分还是test
spk_samples_list[spk] = split_forTrainDevTest(
spk_samples_list[spk], train_or_test)
#这个时候这个spk已经注册了，所以直接从里面选就好了
sample_name = random.sample(spk_samples_list[spk], 1)[0]
spk_samples_list[spk].remove(
sample_name) #取出来一次之后，就把这个人去掉（避免一个batch里某段语音的重复出现）
spk_speech_path = data_path + '/' + spk + '/' + spk + '_speech/' + sample_name + '.wav'
signal, rate = sf.read(
spk_speech_path) # signal 是采样值，rate 是采样频率
if len(signal.shape) > 1:
signal = signal[:, 0]
if rate != config.FRAME_RATE:
# 如果频率不是设定的频率则需要进行转换
signal = resampy.resample(signal,
rate,
config.FRAME_RATE,
filter='kaiser_best')
if signal.shape[0] > config.MAX_LEN: # 根据最大长度裁剪
signal = signal[:config.MAX_LEN]
# 更新混叠语音长度
if signal.shape[0] > mix_len:
mix_len = signal.shape[0]
signal -= np.mean(signal) # 语音信号预处理，先减去均值
signal /= np.max(np.abs(signal)) # 波形幅值预处理，幅值归一化
# 如果需要augment数据的话，先进行随机shift, 以后考虑固定shift
if config.AUGMENT_DATA:
random_shift = random.sample(range(len(signal)), 1)[0]
signal = signal[random_shift:] + signal[:random_shift]
if signal.shape[0] < config.MAX_LEN: # 根据最大长度用 0 补齐,
signal = np.append(
signal, np.zeros(config.MAX_LEN - signal.shape[0]))
if k == 0: #第一个作为目标
aim_spkname.append(aim_spk_k[0])
# aim_spk=eval(re.findall('\d+',aim_spk_k[0])[0])-1 #选定第一个作为目标说话人
#TODO:这里有个问题是spk是从１开始的貌似，这个后面要统一一下 --> 已经解决，构建了spk和idx的双向索引
aim_spk_speech = signal
aim_spkid.append(aim_spkname)
wav_mix = signal
aim_fea_clean = np.transpose(
np.abs(
librosa.core.spectrum.stft(
signal, config.FRAME_LENGTH,
config.FRAME_SHIFT)))
aim_fea.append(aim_fea_clean)
# 把第一个人顺便也注册进去混合dict里
multi_fea_dict_this_sample[spk] = aim_fea_clean
multi_wav_dict_this_sample[spk] = signal
else:
wav_mix = wav_mix + signal # 混叠后的语音
# 这个说话人的语音
some_fea_clean = np.transpose(
np.abs(
librosa.core.spectrum.stft(
signal,
config.FRAME_LENGTH,
config.FRAME_SHIFT,
)))
multi_fea_dict_this_sample[spk] = some_fea_clean
multi_wav_dict_this_sample[spk] = signal
multi_spk_fea_list.append(
multi_fea_dict_this_sample) #把这个sample的dict传进去
multi_spk_wav_list.append(
multi_wav_dict_this_sample) #把这个sample的dict传进去
# 这里采用log 以后可以考虑采用MFCC或GFCC特征做为输入
if config.IS_LOG_SPECTRAL:
feature_mix = np.log(
np.transpose(
np.abs(
librosa.core.spectrum.stft(
wav_mix,
config.FRAME_LENGTH,
config.FRAME_SHIFT,
window=config.WINDOWS))) + np.spacing(1))
else:
feature_mix = np.transpose(
np.abs(
librosa.core.spectrum.stft(
wav_mix,
config.FRAME_LENGTH,
config.FRAME_SHIFT,
)))
mix_speechs[batch_idx, :] = wav_mix
mix_feas.append(feature_mix)
mix_phase.append(
np.transpose(
librosa.core.spectrum.stft(
wav_mix,
config.FRAME_LENGTH,
config.FRAME_SHIFT,
)))
batch_idx += 1
print 'batch_dix:{}/{},'.format(batch_idx, config.BATCH_SIZE),
if batch_idx == config.BATCH_SIZE: #填满了一个batch
mix_feas = np.array(mix_feas)
mix_phase = np.array(mix_phase)
aim_fea = np.array(aim_fea)
# aim_spkid=np.array(aim_spkid)
query = np.array(query)
print '\nspk_list_from_this_gen:{}'.format(aim_spkname)
print 'aim spk list:', [
one.keys() for one in multi_spk_fea_list
]
# print '\nmix_speechs.shape,mix_feas.shape,aim_fea.shape,aim_spkname.shape,query.shape,all_spk_num:'
# print mix_speechs.shape,mix_feas.shape,aim_fea.shape,len(aim_spkname),query.shape,len(all_spk)
if mode == 'global':
all_spk = sorted(all_spk)
dict_spk_to_idx = {
spk: idx
for idx, spk in enumerate(all_spk)
}
dict_idx_to_spk = {
idx: spk
for idx, spk in enumerate(all_spk)
}
yield all_spk,dict_spk_to_idx,dict_idx_to_spk,\
aim_fea.shape[1],aim_fea.shape[2],32,len(all_spk)
#上面的是：语音长度、语音频率、视频分割多少帧 TODO:后面把这个替换了query.shape[1]
elif mode == 'once':
yield {
'mix_wav': mix_speechs,
'mix_feas': mix_feas,
'mix_phase': mix_phase,
'aim_fea': aim_fea,
'aim_spkname': aim_spkname,
'query': query,
'num_all_spk': len(all_spk),
'multi_spk_fea_list': multi_spk_fea_list,
'multi_spk_wav_list': multi_spk_wav_list
}
batch_idx = 0
mix_speechs = np.zeros((config.BATCH_SIZE, config.MAX_LEN))
mix_feas = [] #应该是bs,n_frames,n_fre这么多
mix_phase = []
aim_fea = [] #应该是bs,n_frames,n_fre这么多
aim_spkid = [] #np.zeros(config.BATCH_SIZE)
aim_spkname = []
query = [] #应该是BATCH_SIZE，shape(query)的形式，用list再转换把
multi_spk_fea_list = []
multi_spk_wav_list = []
else:
raise ValueError('No such dataset:{} for Video'.format(
config.DATASET))
#概念刺激
elif config.MODE == 4:
pass
else:
raise ValueError('No such Model:{}'.format(config.MODE))
def solve(self,
problem,
x=None,
mininner=1,
maxinner=None,
Delta_bar=None,
Delta0=None):
man = problem.manifold
verbosity = problem.verbosity
if maxinner is None:
maxinner = man.dim
# Set default Delta_bar and Delta0 separately to deal with additional
# logic: if Delta_bar is provided but not Delta0, let Delta0
# automatically be some fraction of the provided Delta_bar.
if Delta_bar is None:
try:
Delta_bar = man.typicaldist
except NotImplementedError:
Delta_bar = np.sqrt(man.dim)
if Delta0 is None:
Delta0 = Delta_bar / 8
cost = problem.cost
grad = problem.grad
hess = problem.hess
# If no starting point is specified, generate one at random.
if x is None:
x = man.rand()
# Initializations
time0 = time.time()
# k counts the outer (TR) iterations. The semantic is that k counts the
# number of iterations fully executed so far.
k = 0
# Initialize solution and companion measures: f(x), fgrad(x)
fx = cost(x)
fgradx = grad(x)
norm_grad = man.norm(x, fgradx)
# Initialize the trust region radius
Delta = Delta0
# To keep track of consecutive radius changes, so that we can warn the
# user if it appears necessary.
consecutive_TRplus = 0
consecutive_TRminus = 0
# ** Display:
if verbosity >= 1:
print("Optimizing...")
if verbosity >= 2:
print("{:44s}f: {:+.6e} |grad|: {:.6e}".format(
" ", float(fx), norm_grad))
self._start_optlog(extraiterfields=['gradnorm'])
while True:
# *************************
# ** Begin TR Subproblem **
# *************************
if self._logverbosity >= 2:
self._append_optlog(k + 1, x, fx, gradnorm=norm_grad)
# Determine eta0
if not self.use_rand:
# Pick the zero vector
eta = man.zerovec(x)
else:
# Random vector in T_x M (this has to be very small)
eta = 1e-6 * man.randvec(x)
# Must be inside trust region
while man.norm(x, eta) > Delta:
eta = np.sqrt(np.sqrt(np.spacing(1)))
# Solve TR subproblem approximately
eta, Heta, numit, stop_inner = self._truncated_conjugate_gradient(
problem, x, fgradx, eta, Delta, self.theta, self.kappa,
mininner, maxinner)
srstr = self.TCG_STOP_REASONS[stop_inner]
# If using randomized approach, compare result with the Cauchy
# point. Convergence proofs assume that we achieve at least (a
# fraction of) the reduction of the Cauchy point. After this
# if-block, either all eta-related quantities have been changed
# consistently, or none of them have.
if self.use_rand:
used_cauchy = False
# Check the curvature
Hg = hess(x, fgradx)
g_Hg = man.inner(x, fgradx, Hg)
if g_Hg <= 0:
tau_c = 1
else:
tau_c = min(norm_grad**3 / (Delta * g_Hg), 1)
# and generate the Cauchy point.
eta_c = -tau_c * Delta / norm_grad * fgradx
Heta_c = -tau_c * Delta / norm_grad * Hg
# Now that we have computed the Cauchy point in addition to the
# returned eta, we might as well keep the best of them.
mdle = (fx + man.inner(x, fgradx, eta) +
0.5 * man.inner(x, Heta, eta))
mdlec = (fx + man.inner(x, fgradx, eta_c) +
0.5 * man.inner(x, Heta_c, eta_c))
if mdlec < mdle:
eta = eta_c
Heta = Heta_c
used_cauchy = True
# This is only computed for logging purposes, because it may be
# useful for some user-defined stopping criteria. If this is not
# cheap for specific applications (compared to evaluating the
# cost), we should reconsider this.
# norm_eta = man.norm(x, eta)
# Compute the tentative next iterate (the proposal)
x_prop = man.retr(x, eta)
# Compute the function value of the proposal
fx_prop = cost(x_prop)
# Will we accept the proposal or not? Check the performance of the
# quadratic model against the actual cost.
rhonum = fx - fx_prop
rhoden = -man.inner(x, fgradx, eta) - 0.5 * man.inner(x, eta, Heta)
# rhonum could be anything.
# rhoden should be nonnegative, as guaranteed by tCG, baring
# numerical errors.
# Heuristic -- added Dec. 2, 2013 (NB) to replace the former
# heuristic. This heuristic is documented in the book by Conn Gould
# and Toint on trust-region methods, section 17.4.2. rhonum
# measures the difference between two numbers. Close to
# convergence, these two numbers are very close to each other, so
# that computing their difference is numerically challenging: there
# may be a significant loss in accuracy. Since the acceptance or
# rejection of the step is conditioned on the ratio between rhonum
# and rhoden, large errors in rhonum result in a very large error
# in rho, hence in erratic acceptance / rejection. Meanwhile, close
# to convergence, steps are usually trustworthy and we should
# transition to a Newton- like method, with rho=1 consistently. The
# heuristic thus shifts both rhonum and rhoden by a small amount
# such that far from convergence, the shift is irrelevant and close
# to convergence, the ratio rho goes to 1, effectively promoting
# acceptance of the step. The rationale is that close to
# convergence, both rhonum and rhoden are quadratic in the distance
# between x and x_prop. Thus, when this distance is on the order of
# sqrt(eps), the value of rhonum and rhoden is on the order of eps,
# which is indistinguishable from the numerical error, resulting in
# badly estimated rho's.
# For abs(fx) < 1, this heuristic is invariant under offsets of f
# but not under scaling of f. For abs(fx) > 1, the opposite holds.
# This should not alarm us, as this heuristic only triggers at the
# very last iterations if very fine convergence is demanded.
rho_reg = max(1, abs(fx)) * np.spacing(1) * self.rho_regularization
rhonum = rhonum + rho_reg
rhoden = rhoden + rho_reg
# This is always true if a linear, symmetric operator is used for
# the Hessian (approximation) and if we had infinite numerical
# precision. In practice, nonlinear approximations of the Hessian
# such as the built-in finite difference approximation and finite
# numerical accuracy can cause the model to increase. In such
# scenarios, we decide to force a rejection of the step and a
# reduction of the trust-region radius. We test the sign of the
# regularized rhoden since the regularization is supposed to
# capture the accuracy to which rhoden is computed: if rhoden were
# negative before regularization but not after, that should not be
# (and is not) detected as a failure.
#
# Note (Feb. 17, 2015, NB): the most recent version of tCG already
# includes a mechanism to ensure model decrease if the Cauchy step
# attained a decrease (which is theoretically the case under very
# lax assumptions). This being said, it is always possible that
# numerical errors will prevent this, so that it is good to keep a
# safeguard.
#
# The current strategy is that, if this should happen, then we
# reject the step and reduce the trust region radius. This also
# ensures that the actual cost values are monotonically decreasing.
model_decreased = (rhoden >= 0)
if not model_decreased:
srstr = srstr + ", model did not decrease"
try:
rho = rhonum / rhoden
except ZeroDivisionError:
# Added June 30, 2015 following observation by BM. With this
# modification, it is guaranteed that a step rejection is
# always accompanied by a TR reduction. This prevents
# stagnation in this "corner case" (NaN's really aren't
# supposed to occur, but it's nice if we can handle them
# nonetheless).
print("rho is NaN! Forcing a radius decrease. This should "
"not happen.")
rho = np.nan
# Choose the new TR radius based on the model performance
trstr = " "
# If the actual decrease is smaller than 1/4 of the predicted
# decrease, then reduce the TR radius.
if rho < 1.0 / 4 or not model_decreased or np.isnan(rho):
trstr = "TR-"
Delta = Delta / 4
consecutive_TRplus = 0
consecutive_TRminus = consecutive_TRminus + 1
if consecutive_TRminus >= 5 and verbosity >= 1:
consecutive_TRminus = -np.inf
print(" +++ Detected many consecutive TR- (radius "
"decreases).")
print(" +++ Consider decreasing options.Delta_bar "
"by an order of magnitude.")
print(" +++ Current values: Delta_bar = {:g} and "
"Delta0 = {:g}".format(Delta_bar, Delta0))
# If the actual decrease is at least 3/4 of the precicted decrease
# and the tCG (inner solve) hit the TR boundary, increase the TR
# radius. We also keep track of the number of consecutive
# trust-region radius increases. If there are many, this may
# indicate the need to adapt the initial and maximum radii.
elif rho > 3.0 / 4 and (stop_inner == self.NEGATIVE_CURVATURE
or stop_inner == self.EXCEEDED_TR):
trstr = "TR+"
Delta = min(2 * Delta, Delta_bar)
consecutive_TRminus = 0
consecutive_TRplus = consecutive_TRplus + 1
if consecutive_TRplus >= 5 and verbosity >= 1:
consecutive_TRplus = -np.inf
print(" +++ Detected many consecutive TR+ (radius "
"increases).")
print(" +++ Consider increasing options.Delta_bar "
"by an order of magnitude.")
print(" +++ Current values: Delta_bar = {:g} and "
"Delta0 = {:g}.".format(Delta_bar, Delta0))
else:
# Otherwise, keep the TR radius constant.
consecutive_TRplus = 0
consecutive_TRminus = 0
# Choose to accept or reject the proposed step based on the model
# performance. Note the strict inequality.
if model_decreased and rho > self.rho_prime:
# accept = True
accstr = "acc"
x = x_prop
fx = fx_prop
fgradx = grad(x)
norm_grad = man.norm(x, fgradx)
else:
# accept = False
accstr = "REJ"
# k is the number of iterations we have accomplished.
k = k + 1
# ** Display:
if verbosity == 2:
print("{:.3s} {:.3s} k: {:5d} num_inner: "
"{:5d} f: {:+e} |grad|: {:e} "
"{:s}".format(accstr, trstr, k, numit, float(fx),
norm_grad, srstr))
elif verbosity > 2:
if self.use_rand and used_cauchy:
print("USED CAUCHY POINT")
print("{:.3s} {:.3s} k: {:5d} num_inner: "
"{:5d} {:s}".format(accstr, trstr, k, numit, srstr))
print(" f(x) : {:+e} |grad| : "
"{:e}".format(fx, norm_grad))
print(" rho : {:e}".format(rho))
# ** CHECK STOPPING criteria
stop_reason = self._check_stopping_criterion(time0,
gradnorm=norm_grad,
iter=k)
if stop_reason:
if verbosity >= 1:
print(stop_reason)
print('')
break
if self._logverbosity <= 0:
return x
else:
self._stop_optlog(x,
fx,
stop_reason,
time0,
gradnorm=norm_grad,
iter=k)
return x, self._optlog
def solve_nd_trust_region_subproblem(
B: np.ndarray,
g: np.ndarray,
delta: float,
logger: Union[logging.Logger, None] = None) -> Tuple[np.ndarray, str]:
r"""
This function exactly solves the n-dimensional subproblem.
:math:`argmin_s\{s^T B s + s^T g = 0: ||s|| <= \Delta, s \in \mathbb{
R}^n\}`
The solution to is characterized by the equation
:math:`-(B + \lambda I)s = g`. If B is positive definite, the solution can
be obtained by :math:`\lambda = 0`$` if :math:`Bs = -g` satisfies
:math:`||s|| <= \Delta`. If B is indefinite or :math:`Bs = -g`
satisfies :math:`||s|| > \Delta` and an approppriate :math:`\lambda` has
to be identified via 1D rootfinding of the secular equation
:math:`\phi(\lambda) = \frac{1}{||s(\lambda)||} - \frac{1}{\Delta} = 0`
with :math:`s(\lambda)` computed according to an eigenvalue decomposition
of B. The eigenvalue decomposition, although being more expensive than a
cholesky decomposition, has the advantage that eigenvectors are
invariant to changes in :math:`\lambda` and eigenvalues are linear in
:math:`\lambda`, so factorization only has to be performed once. We perform
the linesearch via Newton's algorithm and Brent-Q as fallback.
The hard case is treated seperately and serves as general fallback.
:param B:
Hessian of the quadratic subproblem
:param g:
Gradient of the quadratic subproblem
:param delta:
Norm boundary for the solution of the quadratic subproblem
:param logger:
Logger instance to be used for logging
:return:
s: Selected step,
step_type: Type of solution that was obtained
"""
if logger is None:
logger = logging.getLogger('fides')
if delta == 0:
return np.zeros(g.shape), 'zero'
# See Nocedal & Wright 2006 for details
# INITIALIZATION
# instead of a cholesky factorization, we go with an eigenvalue
# decomposition, which works pretty well for n=2
eigvals, eigvecs = linalg.eig(B)
eigvals = np.real(eigvals)
eigvecs = np.real(eigvecs)
w = -eigvecs.T.dot(g)
jmin = eigvals.argmin()
mineig = eigvals[jmin]
# since B symmetric eigenvecs V are orthonormal
# B + lambda I = V * (E + lambda I) * V.T
# inv(B + lambda I) = V * inv(E + lambda I) * V.T
# w = V.T * g
# s(lam) = V * w./(eigvals + lam)
# ds(lam) = - V * w./((eigvals + lam)**2)
# \phi(lam) = 1/||s(lam)|| - 1/delta
# \phi'(lam) = - s(lam).T*ds(lam)/||s(lam)||^3
# POSITIVE DEFINITE
if mineig > 0: # positive definite
s = np.real(slam(0, w, eigvals, eigvecs)) # s = - self.cB\self.cg_hat
if norm(s) <= delta + np.sqrt(np.spacing(1)): # CASE 0
logger.debug('Interior subproblem solution')
return s, 'posdef'
else:
laminit = 0
else:
laminit = -mineig
# INDEFINITE CASE
# note that this includes what Nocedal calls the "hard case" but with
# ||s|| > delta, so the provided formula is not applicable,
# the respective w should be close to 0 anyways
if secular(laminit, w, eigvals, eigvecs, delta) < 0:
maxiter = 100
try:
r = newton(
secular,
laminit,
dsecular,
tol=1e-12,
maxiter=maxiter,
args=(w, eigvals, eigvecs, delta),
)
s = slam(r, w, eigvals, eigvecs)
if norm(s) <= delta + 1e-12:
logger.debug('Found boundary subproblem solution via newton')
return s, 'indef'
except RuntimeError:
pass
try:
xa = laminit
xb = (laminit + np.sqrt(np.spacing(1))) * 10
# search to the right for a change of sign
while secular(xb, w, eigvals, eigvecs, delta) < 0 and \
maxiter > 0:
xa = xb
xb = xb * 10
maxiter -= 1
if maxiter > 0:
r = brentq(secular,
xa,
xb,
xtol=1e-12,
maxiter=maxiter,
args=(w, eigvals, eigvecs, delta))
s = slam(r, w, eigvals, eigvecs)
if norm(s) <= delta + np.sqrt(np.spacing(1)):
logger.debug(
'Found boundary subproblem solution via brentq')
return s, 'indef'
except RuntimeError:
pass # may end up here due to ill-conditioning, treat as hard case
# HARD CASE (gradient is orthogonal to eigenvector to smallest eigenvalue)
w[(eigvals - mineig) == 0] = 0
s = slam(-mineig, w, eigvals, eigvecs)
# we know that ||s(lam) + sigma*v_jmin|| = delta, since v_jmin is
# orthonormal, we can just substract the difference in norm to get
# the right length.
sigma = np.sqrt(max(delta**2 - norm(s)**2, 0))
s = s + sigma * eigvecs[:, jmin]
logger.debug('Found boundary 2D subproblem solution via hard case')
return s, 'hard'
def s_plm(cls, x, c):
S2 = np.linalg.inv(c.T @ c) @ c.T @ x # Multilinear regression
S2[S2 <= np.spacing(1)] = np.spacing(1) # avoid 0 values
s = S2 / (np.sum(S2, axis=1) * np.ones(
(np.size(S2, axis=1), 1))).T # Normalization
return s
def get_mask(i, W, H):
eps = np.spacing(1)
V = np.dot(W,H)
V1 = np.dot(W[:,i].reshape(-1,1),H[i,:].reshape(1,-1))
return V1/(V+eps)
def test_ufunc_spacing_u(A: dace.uint32[10]):
return np.spacing(A)
def main():
"""Main function of the feature_extractor script.
Loading the audio files and extracting the mel band features.
"""
with open(path.join('feature_params.yml'), 'r') as f:
feature_params = yaml.load(f)
audio_file_dir = feature_params['general']['audio_files_dir']
feature_set_dir = feature_params['general']['feature_set_dir']
if not os.path.exists(path.join(feature_set_dir)):
os.makedirs(path.join(feature_set_dir))
# defining the initial parameters for extracting the features
smpl_rate = feature_params['feature']['sampling_rate']
n_fft_ = feature_params['feature']['n_fft']
win_length = feature_params['feature']['win_length']
hop_length = feature_params['feature']['hop_length']
n_mel_bands = feature_params['feature']['n_mel_bands']
f_min = feature_params['feature']['f_min']
f_max = feature_params['feature']['f_max']
htk_ = feature_params['feature']['htk_']
spectrogram_type = feature_params['feature']['spectrogram_type']
window_name = feature_params['feature']['window_name']
symmetric = feature_params['feature']['symmetric']
if window_name in scipy.signal.windows.__all__:
win_func = eval(window_name)
else:
raise ValueError('Unknown window function')
window = win_func(win_length, sym=symmetric)
mel_basis = librosa.filters.mel(sr=smpl_rate,
n_fft=n_fft_,
n_mels=n_mel_bands,
fmin=f_min,
fmax=f_max,
htk=htk_)
audio_data_path = glob.glob(os.path.join(audio_file_dir, '*.wav'))
eps = np.spacing(1)
for ind, smpl in enumerate(audio_data_path):
wav_smpl, sr_ = sf.read(smpl, always_2d=True)
# to converts wav_smpl[samples, channels] to wav_smpl[channels, samples]
wav_smpl = wav_smpl.T
feature_matrix_container = []
stat_container = []
for channel in range(wav_smpl.shape[0]):
spectrogram_ = spectrogram(y=wav_smpl[channel, :],
n_fft=n_fft_,
win_length=win_length,
hop_length=hop_length,
spectrogram_type=spectrogram_type,
center=True,
window=window)
feature_matrix = np.dot(mel_basis, spectrogram_)
feature_matrix = np.log(feature_matrix + eps)
feature_matrix_container.append(feature_matrix)
stat_container.append({
'mean': np.mean(feature_matrix.T, axis=0),
'std': np.std(feature_matrix.T, axis=0)
})
with open(
path.join(feature_set_dir,
smpl.split('/')[-1].replace('.wav', '.p')),
'wb') as f_name:
pickle.dump(
{
'feat': feature_matrix_container,
'stat': stat_container
},
f_name,
protocol=2)
with open(path.join(feature_set_dir, 'feature_set_params.yaml'), 'w') as f:
yaml.dump(
{
'sample rate': smpl_rate,
'minimum frequency': f_min,
'maximum frequency': f_max,
'window': window_name,
'window length': win_length,
'hop length': hop_length,
'fft length': n_fft_,
'mel bands': n_mel_bands,
'spectrogram type': spectrogram_type,
'htk': htk_
},
f,
default_flow_style=True)
def test_ufunc_spacing_f(A: dace.float32[10]):
return np.spacing(A)
def t1_mp2rage(inv1m_arr=None,
inv1p_arr=None,
inv2m_arr=None,
inv2p_arr=None,
rho_arr=None,
regularization=np.spacing(1),
eff_arr=None,
t1_value_range=(100, 5000),
t1_num=512,
eff_num=32,
**acq_param_kws):
"""
Calculate the T1 map from an MP2RAGE acquisition.
Args:
inv1m_arr (float|np.ndarray): Magnitude of the first inversion image.
inv1p_arr (float|np.ndarray): Phase of the first inversion image.
inv2m_arr (float|np.ndarray): Magnitude of the second inversion image.
inv2p_arr (float|np.ndarray): Phase of the second inversion image.
eff_arr (float|np.array|None): Efficiency of the RF pulse excitation.
This is equivalent to the normalized B1T field.
Note that this must have the same spatial dimensions as the images
acquired with MP2RAGE.
If None, no correction for the RF efficiency is performed.
t1_value_range (tuple[float]): The T1 value range to consider.
The format is (min, max) where min < max.
Values should be positive.
t1_num (int): The base number of sampling points of T1.
The actual number of sampling points is usually smaller, because of
the removal of non-bijective branches.
This affects the precision of the MP2RAGE estimation.
eff_num (int): The base number of sampling points for the RF efficiency.
This affects the precision of the RF efficiency correction.
**acq_param_kws (dict): The acquisition parameters.
This should match the signature of: `mp2rage.acq_to_seq_params`.
Returns:
t1_arr (float|np.ndarray): The calculated T1 map for MP2RAGE.
"""
from pymrt.sequences import mp2rage
import matplotlib.pyplot as plt
if eff_arr:
# todo: implement B1T correction
raise NotImplementedError('B1T correction is not yet implemented')
else:
# determine the signal expression
t1 = np.linspace(t1_value_range[0], t1_value_range[1], t1_num)
seq_param_kws = mp2rage.acq_to_seq_params(**acq_param_kws)[0]
rho = mp2rage.signal(t1, **seq_param_kws)
# plot T1 vs. RHO
plt.figure()
plt.plot(rho, t1)
plt.xlabel('RHO')
plt.ylabel('T1 (ms)')
plt.title('T1 vs. RHO')
plt.savefig('T1_vs_RHO.pdf', format='PDF', transparent=True)
# remove non-bijective branches
bijective_part = pmu.bijective_part(rho)
t1 = t1[bijective_part]
rho = rho[bijective_part]
if rho[0] > rho[-1]:
rho = rho[::-1]
t1 = t1[::-1]
# plot the bijective part of the graph
plt.figure()
plt.plot(rho, t1)
plt.xlabel('RHO')
plt.ylabel('T1 (ms)')
plt.title('T1 vs. RHO (bijective part only)')
plt.savefig('T1_vs_RHO_bij.pdf', format='PDF', transparent=True)
# check that rho values are strictly increasing
if not np.all(np.diff(rho) > 0):
raise ValueError(
'MP2RAGE look-up table was not properly prepared.')
if rho_arr == None:
rho_arr = uniform_mp2rage(inv1m_arr,
inv1p_arr,
inv2m_arr,
inv2p_arr,
regularization,
values_interval=None)
else:
rho_arr = pmu.scale(rho_arr, (-0.5, 0.5), DICOM_INTERVAL)
print(np.min(rho_arr), np.max(rho_arr))
t1_arr = np.interp(rho_arr, rho, t1)
return t1_arr, rho_arr
def get_transition_probabilities(s, x, F, a):
"""Calculate the transition probabilities for the given state and action.
Parameters
----------
s : float
The class-independent probability of the population staying in its
current population abundance class.
x : int
The population abundance class of the threatened species.
F : int
The time in years since last fire.
a : int
The action undertaken.
Returns
-------
prob : array
The transition probabilities as a vector from state (``x``, ``F``) to
every other state given that action ``a`` is taken.
"""
# Check that input is in range
check_probability(s)
check_population_class(x)
check_fire_class(F)
check_action(a)
# a vector to store the transition probabilities
prob = np.zeros(STATES)
# the habitat suitability value
r = get_habitat_suitability(F)
F = transition_fire_state(F, a)
## Population transitions
if x == 0:
# population abundance class stays at 0 (extinct)
new_state = convert_state_to_index(0, F)
prob[new_state] = 1
elif x == POPULATION_CLASSES - 1:
# Population abundance class either stays at maximum or transitions
# down
transition_same = x
transition_down = x - 1
# If action 1 is taken, then the patch is burned so the population
# abundance moves down a class.
if a == ACTION_BURN:
transition_same -= 1
transition_down -= 1
# transition probability that abundance stays the same
new_state = convert_state_to_index(transition_same, F)
prob[new_state] = 1 - (1 - s) * (1 - r)
# transition probability that abundance goes down
new_state = convert_state_to_index(transition_down, F)
prob[new_state] = (1 - s) * (1 - r)
else:
# Population abundance class can stay the same, transition up, or
# transition down.
transition_same = x
transition_up = x + 1
transition_down = x - 1
# If action 1 is taken, then the patch is burned so the population
# abundance moves down a class.
if a == ACTION_BURN:
transition_same -= 1
transition_up -= 1
# Ensure that the abundance class doesn't go to -1
if transition_down > 0:
transition_down -= 1
# transition probability that abundance stays the same
new_state = convert_state_to_index(transition_same, F)
prob[new_state] = s
# transition probability that abundance goes up
new_state = convert_state_to_index(transition_up, F)
prob[new_state] = (1 - s) * r
# transition probability that abundance goes down
new_state = convert_state_to_index(transition_down, F)
# In the case when transition_down = 0 before the effect of an action
# is applied, then the final state is going to be the same as that for
# transition_same, so we need to add the probabilities together.
prob[new_state] += (1 - s) * (1 - r)
# Make sure that the probabilities sum to one
assert (prob.sum() - 1) < np.spacing(1)
return prob
def get_mask1(i, W, H):
eps = np.spacing(1)
V = np.dot(W,H)
V1 = np.dot(W[:,i],H[i,:])
return V1/(V+eps)