def data_normalize_inplace(x, power = 0.5, compute_norm_subset = None):
if compute_norm_subset != None:
x_subset = x[compute_norm_subset]
else:
x_subset = x
mu = x_subset.mean(axis=0)
sigma = (x_subset - mu).std(axis=0)
if np.min(sigma) == 0:
warnings.warn("At least one dimension of the data has zero variance.")
sigma[sigma == 0] = 1.
del x_subset
x -= mu
x *= 1. / sigma
if power == 1:
pass
elif power == 0.5:
yael.fvec_ssqrt(yael.numpy_to_fvec_ref(x), x.size)
else:
yael.fvec_spow(yael.numpy_to_fvec_ref(x), x.size, power)
yael.fmat_normalize_columns_l2sqr_pow(yael.numpy_to_fvec_ref(x), x.shape[1], x.shape[0], -0.5)
return mu, sigma
def scores_to_probas(scores, A, B):
probas = np.empty(scores.size, dtype = np.float32)
scores = scores.astype(np.float32)
libsvm_precomputed.scores_to_probas(A, B, scores.size,
yael.numpy_to_fvec_ref(scores),
yael.numpy_to_fvec_ref(probas))
return probas
def initYaelGmm(self):
self.yael_gmm = yael.gmm_t()
self.yael_gmm.d = self.n_features
self.yael_gmm.k = self.n_components
self.yael_gmm.mu = yael.numpy_to_fvec_ref(self.means_)
self.yael_gmm.sigma = yael.numpy_to_fvec(self.covars_)
self.yael_gmm.w = yael.numpy_to_fvec_ref(self.weights_)
def eval_params(self, params, fold):
train_index, test_index = self.splits[fold][:2]
if len(self.splits[fold]) == 3: # useful for multiclass optimization
cx = self.splits[fold][2]
else:
cx = self.cx
c = params['c']
pos_weight = params['positive_weight']
Kxx = combine_kernels(self.Kxx, params)
dual_coef, bias = libsvm_train(Kxx, cx, train_index,
c = c,
pos_weight = pos_weight)
scores = np.empty(test_index.size, dtype = np.float32)
libsvm_precomputed.mul_matvec_subset(
yael.numpy_to_fvec_ref(Kxx), Kxx.shape[1],
yael.numpy_to_ivec_ref(train_index), train_index.size,
yael.numpy_to_ivec_ref(test_index), test_index.size,
yael.numpy_to_dvec_ref(dual_coef),
yael.numpy_to_fvec_ref(scores))
scores += bias
if self.criterion == 'ap':
perf = average_precision(cx[test_index], scores)
elif self.criterion == 'dcr':
perf = 1 - compute_dcr(cx[test_index], scores)
elif self.criterion == 'sdcr':
perf = 1 - surrogate_dcr(cx[test_index], scores)
else:
assert False
#### microscopic penalizations
# to favor the lowest c among ties
perf -= math.log(c) * 1e-6
# to favor positive_weight == 1
perf -= abs(math.log(pos_weight)) * 1e-6
stats = Stats()
stats.valid_accuracies = np.array([perf])
return stats
def descs_to_sstats(xx, gmm):
""" Converts the descriptors to sufficient statistics.
Inputs
------
xx: array [nr_descs, nr_dimensions]
Data matrix containing the descriptors.
gmm: yael.gmm instance
Mixture of Gaussian object.
Output
------
Q_sum: array [nr_clusters, ]
Averaged posterior probabilities.
"""
K = gmm.k
N = xx.shape[0]
# Compute posterior probabilities using yael.
Q_yael = fvec_new(N * K)
gmm_compute_p(N, numpy_to_fvec_ref(xx), gmm, Q_yael, GMM_FLAGS_W)
Q = fvec_to_numpy(Q_yael, N * K).reshape(N, K)
yael.free(Q_yael)
# Compute statistics.
Q_sum = sum(Q, 0) / N # 1xK
return np.array(Q_sum, dtype=np.float32)
def descs_to_sstats(xx, gmm):
""" Converts the descriptors to sufficient statistics.
Inputs
------
xx: array [nr_descs, nr_dimensions]
Data matrix containing the descriptors.
gmm: yael.gmm instance
Mixture of Gaussian object.
Output
------
sstats: array [nr_clusters + 2 * nr_clusters * nr_dimensions, ]
Concatenation of the averaged posterior probabilities `Q_sum`, the
first moment `Q_xx` and second-order moment `Q_xx_2`.
"""
xx = np.atleast_2d(xx)
N = xx.shape[0]
K = gmm.k
D = gmm.d
# Compute posterior probabilities using yael.
Q_yael = fvec_new(N * K)
gmm_compute_p(N, numpy_to_fvec_ref(xx), gmm, Q_yael, GMM_FLAGS_W)
Q = fvec_to_numpy(Q_yael, N * K).reshape(N, K) # NxK
yael.free(Q_yael)
# Compute statistics.
sstats = np.zeros(K + 2 * K * D, dtype=np.float32)
sstats[: K] = np.sum(Q, 0) / N # 1xK
sstats[K: K + K * D] = dot(Q.T, xx).flatten() / N # 1xKD
sstats[K + K * D: K + 2 * K * D] = dot(
Q.T, xx ** 2).flatten() / N # 1xKD
return sstats
def descs_to_sstats(xx, gmm):
""" Converts the descriptors to sufficient statistics.
Inputs
------
xx: array [nr_descs, nr_dimensions]
Data matrix containing the descriptors.
gmm: yael.gmm instance
Mixture of Gaussian object.
Output
------
Q_sum: array [nr_clusters, ]
Averaged posterior probabilities.
"""
K = gmm.k
N = xx.shape[0]
# Compute posterior probabilities using yael.
Q_yael = fvec_new(N * K)
gmm_compute_p(N, numpy_to_fvec_ref(xx), gmm, Q_yael, GMM_FLAGS_W)
Q = fvec_to_numpy(Q_yael, N * K).reshape(N, K)
yael.free(Q_yael)
# Compute statistics.
Q_sum = sum(Q, 0) / N # 1xK
return np.array(Q_sum, dtype=np.float32)
def compute_gmm(data, nr_clusters, nr_iterations, nr_threads, seed, nr_redos):
"""Computes GMM using yael functions."""
N, D = data.shape
data = np.ascontiguousarray(data)
return gmm_learn(
D, N, nr_clusters, nr_iterations, numpy_to_fvec_ref(data), nr_threads,
seed, nr_redos, GMM_FLAGS_W)
def descs_to_sstats(xx, gmm):
""" Converts the descriptors to sufficient statistics.
Inputs
------
xx: array [nr_descs, nr_dimensions]
Data matrix containing the descriptors.
gmm: yael.gmm instance
Mixture of Gaussian object.
Output
------
sstats: array [nr_clusters + 2 * nr_clusters * nr_dimensions, ]
Concatenation of the averaged posterior probabilities `Q_sum`, the
first moment `Q_xx` and second-order moment `Q_xx_2`.
"""
xx = np.atleast_2d(xx)
N = xx.shape[0]
K = gmm.k
D = gmm.d
# Compute posterior probabilities using yael.
Q_yael = fvec_new(N * K)
gmm_compute_p(N, numpy_to_fvec_ref(xx), gmm, Q_yael, GMM_FLAGS_W)
Q = fvec_to_numpy(Q_yael, N * K).reshape(N, K) # NxK
yael.free(Q_yael)
# Compute statistics.
sstats = np.zeros(K + 2 * K * D, dtype=np.float32)
sstats[:K] = np.sum(Q, 0) / N # 1xK
sstats[K:K + K * D] = dot(Q.T, xx).flatten() / N # 1xKD
sstats[K + K * D:K + 2 * K * D] = dot(Q.T, xx**2).flatten() / N # 1xKD
return sstats
def gmm_predict_proba(xx, gmm):
"""Computes posterior probabilities using yael."""
N = xx.shape[0]
K = gmm.k
Q_yael = yael.fvec_new(N * K)
yael.gmm_compute_p(
N, yael.numpy_to_fvec_ref(xx), gmm, Q_yael, yael.GMM_FLAGS_W)
Q = yael.fvec_to_numpy(Q_yael, N * K).reshape(N, K)
yael.free(Q_yael)
return Q
def libsvm_train(Kxx, cx, subset, c, pos_weight = 1.0, eps = 1e-3, verbose = 0, probability = 0):
# check input
assert Kxx.shape[0] == Kxx.shape[1] and Kxx.flags.c_contiguous, Kxx.shape
assert subset.flags.c_contiguous and cx.flags.c_contiguous
assert np.all(subset < Kxx.shape[0]) and np.all(subset >= 0)
# set libsvm params
param = libsvm_precomputed.svm_parameter()
libsvm_precomputed.svm_param_set_default(param)
param.nr_weight = 2
param.weight_label = weight_label = yael.ivec(2)
weight_label[0] = -1
weight_label[1] = 1
param.weight = weights = yael.dvec(2)
npos = (cx[subset] == 1).sum()
nneg = (cx[subset] == -1).sum()
weights[0] = 2 * npos / float(npos + nneg)
weights[1] = 2 * nneg / float(npos + nneg) * pos_weight
param.C = c
param.nu = param.p = 0
param.shrinking = 1
param.probability = probability
param.eps = eps
libsvm_precomputed.svm_set_verbose(verbose)
# prepare output
nex = subset.size
dual_coeffs = np.empty((nex,), dtype = np.float64)
bias_out = yael.dvec(3)
# actual call
ret = libsvm_precomputed.svm_train_precomputed(
nex,
yael.numpy_to_ivec_ref(subset),
yael.numpy_to_ivec_ref(cx),
yael.numpy_to_fvec_ref(Kxx),
Kxx.shape[1],
param,
yael.numpy_to_dvec_ref(dual_coeffs),
bias_out)
assert ret > 0
bias_term = bias_out[0]
#print dual_coeffs, bias_term
if probability:
probA = bias_out[1]
probB = bias_out[2]
return dual_coeffs, bias_term, probA, probB
else:
return dual_coeffs, bias_term
def fit(self, X):
n_samples, self.n_features = X.shape
yael_X = yael.numpy_to_fvec_ref(X)
yael_gmm = yael.gmm_learn(
self.n_features, n_samples, self.n_components, self.n_iter, yael_X,
self.n_threads, 0, self.n_init,
yael.GMM_FLAGS_W | yael.GMM_FLAGS_SIGMA | yael.GMM_FLAGS_MU)
self.means_ = yael.fvec_to_numpy(
yael_gmm.mu, self.n_components * self.n_features).reshape(
(self.n_components, self.n_features))
self.covars_ = yael.fvec_to_numpy(
yael_gmm.sigma, self.n_components * self.n_features).reshape(
(self.n_components, self.n_features))
self.weights_ = yael.fvec_to_numpy(yael_gmm.w, self.n_components)
yael.gmm_delete(yael_gmm)
def computeResponsabilities(self, X):
if self.yael_gmm is None:
self.initYaelGmm()
if len(X.shape) == 1:
n_samples = 1
else:
n_samples = X.shape[0]
yael_X = yael.numpy_to_fvec_ref(X)
yael_p = yael.fvec_new_0(self.n_components * n_samples)
yael.gmm_compute_p_thread(
n_samples, yael_X, self.yael_gmm, yael_p,
yael.GMM_FLAGS_W | yael.GMM_FLAGS_SIGMA | yael.GMM_FLAGS_MU,
self.n_threads)
return yael.fvec_to_numpy_acquire(yael_p, n_samples *
self.n_components).reshape(
(n_samples, self.n_components))
def descs_to_spatial_sstats(xx, ll, gmm):
""" Computes spatial statistics from descriptors and their position.
Inputs
------
xx: array [N, D], required
N D-dimensional descriptors from an video (usually, after they are
processed with PCA).
ll: array [N, 3], required
Descriptor locations in an image; on each row, we have the triplet
(x, y, t).
gmm: instance of yael object gmm
Gauassian mixture object.
Output
------
ss: array [1, K + 2 * 3 * k]
Sufficient statistics in the form of a vector that concatenates
(i) the sum of posteriors, (ii) an expected value of the locations
ll under the posterior distribution Q and (iii) the second-order
moment of the locations ll under the posterior distribution Q.
"""
N = ll.shape[0]
K = gmm.k
# Compute posterior probabilities using yael.
Q_yael = fvec_new(N * K)
gmm_compute_p(N, numpy_to_fvec_ref(xx), gmm, Q_yael, GMM_FLAGS_W)
Q = fvec_to_numpy(Q_yael, N * K).reshape(N, K)
yael.free(Q_yael)
# Compute statistics.
Q_sum = sum(Q, 0) / N # 1xK
Q_ll = dot(Q.T, ll).flatten() / N # 1x3K
Q_ll_2 = dot(Q.T, ll**2).flatten() / N # 1x3K
return np.array(hstack((Q_sum, Q_ll, Q_ll_2)), dtype=np.float32)
def descs_to_spatial_sstats(xx, ll, gmm):
""" Computes spatial statistics from descriptors and their position.
Inputs
------
xx: array [N, D], required
N D-dimensional descriptors from an video (usually, after they are
processed with PCA).
ll: array [N, 3], required
Descriptor locations in an image; on each row, we have the triplet
(x, y, t).
gmm: instance of yael object gmm
Gauassian mixture object.
Output
------
ss: array [1, K + 2 * 3 * k]
Sufficient statistics in the form of a vector that concatenates
(i) the sum of posteriors, (ii) an expected value of the locations
ll under the posterior distribution Q and (iii) the second-order
moment of the locations ll under the posterior distribution Q.
"""
N = ll.shape[0]
K = gmm.k
# Compute posterior probabilities using yael.
Q_yael = fvec_new(N * K)
gmm_compute_p(N, numpy_to_fvec_ref(xx), gmm, Q_yael, GMM_FLAGS_W)
Q = fvec_to_numpy(Q_yael, N * K).reshape(N, K)
yael.free(Q_yael)
# Compute statistics.
Q_sum = sum(Q, 0) / N # 1xK
Q_ll = dot(Q.T, ll).flatten() / N # 1x3K
Q_ll_2 = dot(Q.T, ll ** 2).flatten() / N # 1x3K
return np.array(hstack((Q_sum, Q_ll, Q_ll_2)), dtype=np.float32)
print('-> Yael A')
yael_a = yael.FloatArray.acquirepointer(yael.numpy_to_fvec(numpy_a))
n = numpy_a.size
yael.fvec_print(yael_a, n)
print('-> Numpy A')
print(yael.fvec_to_numpy(yael_a, n))
print('int array')
numpy_a = np.array(list(range(5)), dtype='int32')
print(numpy_a)
print('-> Yael A2')
yael_a = yael.IntArray.acquirepointer(yael.numpy_to_ivec(numpy_a))
n = numpy_a.size
yael.ivec_print(yael_a, n)
print('-> Numpy A2')
print(yael.ivec_to_numpy(yael_a, n))
print('float array, pass by reference')
numpy_a = np.array(list(range(5)), dtype='float32')
print(numpy_a)
if '--force-crash' in sys.argv:
yael_a = yael.FloatArray.acquirepointer(yael.numpy_to_fvec_ref(numpy_a))
n = numpy_a.size
yael.fvec_print(yael_a, n)
del numpy_a
print('Forced Crash Example!')
yael.fvec_print(yael_a, n)
def compute_gmm(data, nr_clusters, nr_iterations, nr_threads, seed, nr_redos):
""" Computes GMM using yael functions. """
N, D = data.shape
gmm = gmm_learn(D, N, nr_clusters, nr_iterations, numpy_to_fvec_ref(data),
nr_threads, seed, nr_redos, GMM_FLAGS_W)
return gmm
