def __init__(self,
X,
y,
n_importance,
prior_log_pdf,
ridge=0.,
num_shogun_threads=1):
self.n_importance = n_importance
self.prior_log_pdf = prior_log_pdf
self.ridge = ridge
self.X = X
self.y = y
self.num_shogun_threads = num_shogun_threads
# tell shogun to use 1 thread only
logger.debug("Using Shogun with %d threads" % self.num_shogun_threads)
sg.ZeroMean().parallel.set_num_threads(self.num_shogun_threads)
# shogun representation of data
self.sg_labels = sg.BinaryLabels(self.y)
self.sg_feats_train = sg.RealFeatures(self.X.T)
# ARD: set set theta, which is in log-scale, as kernel weights
self.sg_kernel = sg.GaussianARDKernel(10, 1)
self.sg_mean = sg.ZeroMean()
self.sg_likelihood = sg.LogitLikelihood()
def sha1sum(fname, blocksize=65536):
"""
Computes sha1sum of the given file. Same as the unix command line hash.
Returns: string with the hex-formatted sha1sum hash
"""
hasher = hashlib.sha1()
with open(fname, 'rb') as afile:
logger.debug("Hasing %s" % fname)
buf = afile.read(blocksize)
while len(buf) > 0:
hasher.update(buf)
buf = afile.read(blocksize)
return hasher.hexdigest()
def sha1sum(fname, blocksize=65536):
"""
Computes sha1sum of the given file. Same as the unix command line hash.
Returns: string with the hex-formatted sha1sum hash
"""
hasher = hashlib.sha1()
with open(fname, 'rb') as afile:
logger.debug("Hasing %s" % fname)
buf = afile.read(blocksize)
while len(buf) > 0:
hasher.update(buf)
buf = afile.read(blocksize)
return hasher.hexdigest()
def __init__(self, X, y, n_importance, prior_log_pdf, ridge=0., num_shogun_threads=1):
self.n_importance = n_importance
self.prior_log_pdf = prior_log_pdf
self.ridge = ridge
self.X = X
self.y = y
self.num_shogun_threads = num_shogun_threads
# tell shogun to use 1 thread only
logger.debug("Using Shogun with %d threads" % self.num_shogun_threads)
sg.ZeroMean().parallel.set_num_threads(self.num_shogun_threads)
# shogun representation of data
self.sg_labels = sg.BinaryLabels(self.y)
self.sg_feats_train = sg.RealFeatures(self.X.T)
# ARD: set set theta, which is in log-scale, as kernel weights
self.sg_kernel = sg.GaussianARDKernel(10, 1)
self.sg_mean = sg.ZeroMean()
self.sg_likelihood = sg.LogitLikelihood()
