def __init__(self,
X,
Y,
regparam=1.0,
kernel='LinearKernel',
basis_vectors=None,
**kwargs):
Y = array_tools.as_2d_array(Y)
self.Y = np.mat(Y)
if X.shape[0] != Y.shape[0]:
raise Exception("First dimension of X and Y must be the same")
if basis_vectors != None:
if X.shape[1] != basis_vectors.shape[1]:
raise Exception(
"Number of columns for X and basis_vectors must be the same"
)
kwargs["bias"] = 0.
kwargs['kernel'] = kernel
kwargs['X'] = X
if basis_vectors != None:
kwargs['basis_vectors'] = basis_vectors
self.svdad = creators.createSVDAdapter(**kwargs)
self.regparam = regparam
self.svals = self.svdad.svals
self.svecs = self.svdad.rsvecs
self.size = self.Y.shape[0]
self.solve(self.regparam)
def createLearner(cls, **kwargs):
new_kwargs = {}
new_kwargs["svdad"] = creators.createSVDAdapter(**kwargs)
new_kwargs["train_labels"] = kwargs["train_labels"]
if kwargs.has_key("regparam"):
new_kwargs['regparam'] = kwargs["regparam"]
learner = cls(**new_kwargs)
return learner
def createLearner(cls, **kwargs):
new_kwargs = {}
new_kwargs["svdad"] = creators.createSVDAdapter(**kwargs)
new_kwargs["train_labels"] = kwargs["train_labels"]
if kwargs.has_key("regparam"):
new_kwargs['regparam'] = kwargs["regparam"]
learner = cls(**new_kwargs)
return learner
def __init__(self, **kwargs):
self.svdad = creators.createSVDAdapter(**kwargs)
self.Y = array_tools.as_labelmatrix(kwargs["train_labels"])
if kwargs.has_key("regparam"):
self.regparam = float(kwargs["regparam"])
else:
self.regparam = 1.
self.svals = self.svdad.svals
self.svecs = self.svdad.rsvecs
self.results = {}
def __init__(self, **kwargs):
self.svdad = creators.createSVDAdapter(**kwargs)
self.Y = array_tools.as_labelmatrix(kwargs["train_labels"])
if kwargs.has_key("regparam"):
self.regparam = float(kwargs["regparam"])
else:
self.regparam = 1.
self.svals = self.svdad.svals
self.svecs = self.svdad.rsvecs
self.results = {}
def createLearner(cls, **kwargs):
new_kwargs = {}
new_kwargs["svdad"] = creators.createSVDAdapter(**kwargs)
if kwargs.has_key("regparam"):
new_kwargs['regparam'] = float(kwargs["regparam"])
if kwargs.has_key("train_labels"):
new_kwargs["train_labels"] = kwargs["train_labels"]
if kwargs.has_key("number_of_clusters"):
new_kwargs['number_of_clusters'] = int(kwargs["number_of_clusters"])
if kwargs.has_key("fixed_indices"):
new_kwargs["fixed_indices"] = kwargs["fixed_indices"]
if kwargs.has_key("callback_obj"):
new_kwargs["callback_obj"] = kwargs["callback_obj"]
learner = cls(**new_kwargs)
return learner
def createLearner(cls, **kwargs):
new_kwargs = {}
new_kwargs["svdad"] = creators.createSVDAdapter(**kwargs)
if kwargs.has_key("regparam"):
new_kwargs['regparam'] = float(kwargs["regparam"])
if kwargs.has_key("train_labels"):
new_kwargs["train_labels"] = kwargs["train_labels"]
if kwargs.has_key("number_of_clusters"):
new_kwargs['number_of_clusters'] = int(
kwargs["number_of_clusters"])
if kwargs.has_key("fixed_indices"):
new_kwargs["fixed_indices"] = kwargs["fixed_indices"]
if kwargs.has_key("callback_obj"):
new_kwargs["callback_obj"] = kwargs["callback_obj"]
learner = cls(**new_kwargs)
return learner
def __init__(self, X, pairs_start_inds, pairs_end_inds, regparam = 1.0, kernel='LinearKernel', basis_vectors = None, **kwargs):
kwargs['kernel'] = kernel
kwargs['X'] = X
if basis_vectors != None:
kwargs["basis_vectors"] = basis_vectors
self.regparam = regparam
self.pairs = np.vstack([pairs_start_inds, pairs_end_inds]).T
self.svdad = creators.createSVDAdapter(**kwargs)
self.svals = self.svdad.svals
self.svecs = self.svdad.rsvecs
self.results = {}
self.X = csc_matrix(X)
self.bias = 0.
self.results = {}
self.solve(regparam)
def __init__(self,
X,
regparam=1.0,
number_of_clusters=2,
kernel='LinearKernel',
basis_vectors=None,
Y=None,
fixed_indices=None,
callback=None,
**kwargs):
kwargs['X'] = X
kwargs['kernel'] = kernel
if basis_vectors != None:
kwargs['basis_vectors'] = basis_vectors
self.svdad = creators.createSVDAdapter(**kwargs)
self.svals = self.svdad.svals
self.svecs = self.svdad.rsvecs
self.callbackfun = callback
self.regparam = regparam
self.constraint = 0
self.labelcount = number_of_clusters
self.size = X.shape[0]
#if self.labelcount == 2:
# self.oneclass = True
#else:
# self.oneclass = False
if Y == None:
self.classvec = np.zeros(self.size)
else:
self.classvec = Y
#self.size = self.classvec.shape[0]
self.Y = -np.ones((self.size, self.labelcount))
self.classcounts = np.zeros((self.labelcount), dtype=np.int32)
for i in range(self.size):
clazzind = self.classvec[i]
self.Y[i, clazzind] = 1
self.classcounts[clazzind] = self.classcounts[clazzind] + 1
self.fixedindices = []
if fixed_indices != None:
self.fixedindices = fixed_indices
self.train()
def __init__(self, **kwargs):
super(PPRankRLS, self).__init__(**kwargs)
if kwargs.has_key("regparam"):
self.regparam = float(kwargs["regparam"])
else:
self.regparam = 0.
self.pairs = kwargs['train_preferences']
self.learn_from_labels = False
self.svdad = creators.createSVDAdapter(**kwargs)
#self.Y = array_tools.as_labelmatrix(kwargs["train_labels"])
#if kwargs.has_key("regparam"):
# self.regparam = float(kwargs["regparam"])
#else:
# self.regparam = 1.
self.svals = self.svdad.svals
self.svecs = self.svdad.rsvecs
self.results = {}
X = kwargs['train_features']
self.X = csc_matrix(X)
self.bias = 0.
self.results = {}
def __init__(self,
X,
Y,
qids,
regparam=1.0,
kernel='LinearKernel',
basis_vectors=None,
**kwargs):
kwargs["bias"] = 0.
kwargs['kernel'] = kernel
kwargs['X'] = X
if basis_vectors != None:
kwargs['basis_vectors'] = basis_vectors
self.svdad = creators.createSVDAdapter(**kwargs)
self.Y = np.mat(array_tools.as_2d_array(Y))
self.regparam = regparam
self.svals = self.svdad.svals
self.svecs = self.svdad.rsvecs
self.size = self.Y.shape[0]
self.size = self.Y.shape[0]
self.qids = map_qids(qids)
self.qidlist = qids_to_splits(self.qids)
self.solve(self.regparam)
def __init__(self, **kwargs):
self.svdad = creators.createSVDAdapter(**kwargs)
self.svals = self.svdad.svals
self.svecs = self.svdad.rsvecs
self.regparam = float(kwargs["regparam"])
self.constraint = 0
#if not kwargs.has_key('number_of_clusters'):
# raise Exception("Parameter 'number_of_clusters' must be given.")
if kwargs.has_key("number_of_clusters"):
self.labelcount = int(kwargs["number_of_clusters"])
else:
self.labelcount = 2
if self.labelcount == 2:
self.oneclass = True
else:
self.oneclass = False
if kwargs.has_key("callback"):
self.callbackfun = kwargs["callback"]
else:
self.callbackfun = None
if kwargs.has_key("train_labels"):
train_labels = kwargs["train_labels"]
else:
train_labels = None
if train_labels != None:
#Y_orig = array_tools.as_labelmatrix(train_labels)
Y_orig = array_tools.as_array(train_labels)
#if Y_orig.shape[1] == 1:
if len(Y_orig.shape) == 1:
self.Y = zeros((Y_orig.shape[0], 2))
self.Y[:, 0] = Y_orig
self.Y[:, 1] = -Y_orig
self.oneclass = True
else:
self.Y = Y_orig.copy()
self.oneclass = False
for i in range(self.Y.shape[0]):
largestind = 0
largestval = self.Y[i, 0]
for j in range(self.Y.shape[1]):
if self.Y[i, j] > largestval:
largestind = j
largestval = self.Y[i, j]
self.Y[i, j] = -1.
self.Y[i, largestind] = 1.
else:
size = self.svecs.shape[0]
ysize = self.labelcount
if self.labelcount == None: self.labelcount = 2
self.Y = RandomLabelSource(size, ysize).readLabels()
self.size = self.Y.shape[0]
self.labelcount = self.Y.shape[1]
self.classvec = -ones((self.size), dtype=int32)
self.classcounts = zeros((self.labelcount), dtype=int32)
for i in range(self.size):
clazzind = 0
largestlabel = self.Y[i, 0]
for j in range(self.labelcount):
if self.Y[i, j] > largestlabel:
largestlabel = self.Y[i, j]
clazzind = j
self.classvec[i] = clazzind
self.classcounts[clazzind] = self.classcounts[clazzind] + 1
self.svecs_list = []
for i in range(self.size):
self.svecs_list.append(self.svecs[i].T)
self.fixedindices = []
if kwargs.has_key("fixed_indices"):
self.fixedindices = kwargs["fixed_indices"]
else:
self.fixedindices = []
self.results = {}
def __init__(self, **kwargs):
self.svdad = creators.createSVDAdapter(**kwargs)
self.svals = self.svdad.svals
self.svecs = self.svdad.rsvecs
self.regparam = float(kwargs["regparam"])
self.constraint = 0
#if not kwargs.has_key('number_of_clusters'):
# raise Exception("Parameter 'number_of_clusters' must be given.")
if kwargs.has_key("number_of_clusters"):
self.labelcount = int(kwargs["number_of_clusters"])
else:
self.labelcount = 2
if self.labelcount == 2:
self.oneclass = True
else:
self.oneclass = False
if kwargs.has_key("callback"):
self.callbackfun = kwargs["callback"]
else:
self.callbackfun = None
if kwargs.has_key("train_labels"):
train_labels = kwargs["train_labels"]
else:
train_labels = None
if train_labels != None:
#Y_orig = array_tools.as_labelmatrix(train_labels)
Y_orig = array_tools.as_array(train_labels)
#if Y_orig.shape[1] == 1:
if len(Y_orig.shape) == 1:
self.Y = zeros((Y_orig.shape[0], 2))
self.Y[:, 0] = Y_orig
self.Y[:, 1] = - Y_orig
self.oneclass = True
else:
self.Y = Y_orig.copy()
self.oneclass = False
for i in range(self.Y.shape[0]):
largestind = 0
largestval = self.Y[i, 0]
for j in range(self.Y.shape[1]):
if self.Y[i, j] > largestval:
largestind = j
largestval = self.Y[i, j]
self.Y[i, j] = -1.
self.Y[i, largestind] = 1.
else:
size = self.svecs.shape[0]
ysize = self.labelcount
if self.labelcount == None: self.labelcount = 2
self.Y = RandomLabelSource(size, ysize).readLabels()
self.size = self.Y.shape[0]
self.labelcount = self.Y.shape[1]
self.classvec = - ones((self.size), dtype = int32)
self.classcounts = zeros((self.labelcount), dtype = int32)
for i in range(self.size):
clazzind = 0
largestlabel = self.Y[i, 0]
for j in range(self.labelcount):
if self.Y[i, j] > largestlabel:
largestlabel = self.Y[i, j]
clazzind = j
self.classvec[i] = clazzind
self.classcounts[clazzind] = self.classcounts[clazzind] + 1
self.svecs_list = []
for i in range(self.size):
self.svecs_list.append(self.svecs[i].T)
self.fixedindices = []
if kwargs.has_key("fixed_indices"):
self.fixedindices = kwargs["fixed_indices"]
else:
self.fixedindices = []
self.results = {}
def __init__(self,
X,
regparam=1.0,
number_of_clusters=2,
kernel='LinearKernel',
basis_vectors=None,
Y=None,
fixed_indices=None,
callback=None,
**kwargs):
kwargs['X'] = X
kwargs['kernel'] = kernel
if basis_vectors != None:
kwargs['basis_vectors'] = basis_vectors
self.svdad = creators.createSVDAdapter(**kwargs)
self.svals = self.svdad.svals
self.svecs = self.svdad.rsvecs
self.regparam = regparam
self.constraint = 0
#if not kwargs.has_key('number_of_clusters'):
# raise Exception("Parameter 'number_of_clusters' must be given.")
self.labelcount = number_of_clusters
if self.labelcount == 2:
self.oneclass = True
else:
self.oneclass = False
self.callbackfun = callback
if Y != None:
Y_orig = array_tools.as_array(Y)
#if Y_orig.shape[1] == 1:
if len(Y_orig.shape) == 1:
self.Y = np.zeros((Y_orig.shape[0], 2))
self.Y[:, 0] = Y_orig
self.Y[:, 1] = -Y_orig
self.oneclass = True
else:
self.Y = Y_orig.copy()
self.oneclass = False
for i in range(self.Y.shape[0]):
largestind = 0
largestval = self.Y[i, 0]
for j in range(self.Y.shape[1]):
if self.Y[i, j] > largestval:
largestind = j
largestval = self.Y[i, j]
self.Y[i, j] = -1.
self.Y[i, largestind] = 1.
else:
size = self.svecs.shape[0]
ysize = self.labelcount
if self.labelcount == None: self.labelcount = 2
self.Y = RandomLabelSource(size, ysize).readLabels()
self.size = self.Y.shape[0]
self.labelcount = self.Y.shape[1]
self.classvec = -np.ones((self.size), dtype=np.int32)
self.classcounts = np.zeros((self.labelcount), dtype=np.int32)
for i in range(self.size):
clazzind = 0
largestlabel = self.Y[i, 0]
for j in range(self.labelcount):
if self.Y[i, j] > largestlabel:
largestlabel = self.Y[i, j]
clazzind = j
self.classvec[i] = clazzind
self.classcounts[clazzind] = self.classcounts[clazzind] + 1
self.svecs_list = []
for i in range(self.size):
self.svecs_list.append(self.svecs[i].T)
self.fixedindices = []
if fixed_indices != None:
self.fixedindices = fixed_indices
else:
self.fixedindices = []
self.results = {}
self.solve(self.regparam)
