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 is not None:
kwargs['basis_vectors'] = basis_vectors
self.svdad = adapter.createSVDAdapter(**kwargs)
self.svals = np.mat(self.svdad.svals)
self.svecs = np.mat(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 is None:
self.classvec = np.zeros(self.size, np.int)
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 is not None:
self.fixedindices = fixed_indices
self.train()
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 is not None:
kwargs['basis_vectors'] = basis_vectors
self.svdad = adapter.createSVDAdapter(**kwargs)
self.svals = np.mat(self.svdad.svals)
self.svecs = np.mat(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 is None:
self.classvec = np.zeros(self.size, np.int)
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 is not None:
self.fixedindices = fixed_indices
self.train()
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 is not 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 is not None:
kwargs['basis_vectors'] = basis_vectors
self.svdad = adapter.createSVDAdapter(**kwargs)
self.regparam = regparam
self.svals = np.mat(self.svdad.svals)
self.svecs = np.mat(self.svdad.rsvecs)
self.size = self.Y.shape[0]
self.solve(self.regparam)
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 is not None:
kwargs['basis_vectors'] = basis_vectors
self.svdad = adapter.createSVDAdapter(**kwargs)
self.Y = np.mat(array_tools.as_2d_array(Y))
self.regparam = regparam
self.svals = np.mat(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, X, Y, regparam = 1.0, kernel='LinearKernel', basis_vectors = None, **kwargs):
self.Y = array_tools.as_2d_array(Y)
if X.shape[0] != Y.shape[0]:
raise Exception("First dimension of X and Y must be the same")
if basis_vectors is not None:
if X.shape[1] != basis_vectors.shape[1]:
raise Exception("Number of columns for X and basis_vectors must be the same")
kwargs['X'] = X
kwargs['kernel'] = kernel
if basis_vectors is not None:
kwargs['basis_vectors'] = basis_vectors
self.svdad = adapter.createSVDAdapter(**kwargs)
self.regparam = regparam
self.svals = np.mat(self.svdad.svals)
self.svecs = np.mat(self.svdad.rsvecs)
self.size = self.Y.shape[0]
self.solve(self.regparam)
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 is not None:
kwargs["basis_vectors"] = basis_vectors
self.regparam = regparam
self.pairs = np.vstack([pairs_start_inds, pairs_end_inds]).T
self.svdad = adapter.createSVDAdapter(**kwargs)
self.svals = np.mat(self.svdad.svals)
self.svecs = self.svdad.rsvecs
self.results = {}
self.X = csc_matrix(X)
self.bias = 0.0
self.results = {}
self.solve(regparam)
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 is not None:
kwargs['basis_vectors'] = basis_vectors
self.svdad = adapter.createSVDAdapter(**kwargs)
self.Y = np.mat(array_tools.as_2d_array(Y))
self.regparam = regparam
self.svals = np.mat(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,
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 is not None:
kwargs["basis_vectors"] = basis_vectors
self.regparam = regparam
self.pairs = np.vstack([pairs_start_inds, pairs_end_inds]).T
self.svdad = adapter.createSVDAdapter(**kwargs)
self.svals = np.mat(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 is not None:
kwargs['basis_vectors'] = basis_vectors
self.svdad = adapter.createSVDAdapter(**kwargs)
self.svals = np.mat(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 is not 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 is 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 is not None:
self.fixedindices = fixed_indices
else:
self.fixedindices = []
self.results = {}
self.solve(self.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 is not None:
kwargs['basis_vectors'] = basis_vectors
self.svdad = adapter.createSVDAdapter(**kwargs)
self.svals = np.mat(self.svdad.svals)
self.svecs = np.mat(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 is None:
self.classvec = np.zeros(self.size, np.int)
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 is not None:
self.fixedindices = fixed_indices
#Cached results
self.evals = np.multiply(self.svals, self.svals)
self.newevals = 1. / (self.evals + self.regparam)
newevalslamtilde = np.multiply(self.evals, self.newevals)
self.D = np.sqrt(newevalslamtilde)
#self.D = -newevalslamtilde
self.VTY = self.svecs.T * self.Y
self.DVTY = np.multiply(self.D.T, self.svecs.T * self.Y)
self.sqrtR = np.multiply(np.sqrt(newevalslamtilde), self.svecs)
#self.R = self.svecs * multiply(newevalslamtilde.T, self.svecs.T)
'''
#Global variation
self.R = self.sqrtR * self.sqrtR.T
self.minus_diagRx2 = - 2 * np.diag(self.R)
'''
#Space efficient variation
self.R = None
self.minus_diagRx2 = - 2 * np.array(np.sum(np.multiply(self.sqrtR, self.sqrtR), axis = 1)).reshape((self.size))
#'''
self.RY = self.sqrtR * (self.sqrtR.T * self.Y)
self.Y_Schur_RY = np.multiply(self.Y, self.RY)
self.classFitnessList = []
for i in range(self.labelcount):
#DVTY_i = DVTY[:,i]
#self.DVTY_list.append(DVTY_i)
YTRY_i = self.Y[:,i].T * self.RY[:,i]
#self.YTRY_list.append(YTRY_i)
fitness_i = self.size - YTRY_i
self.classFitnessList.append(fitness_i[0, 0])
self.classFitnessRowVec = np.array(self.classFitnessList)
if not self.callbackfun is None:
self.callbackfun.callback(self)
#Initial working set contains all points
self.new_working_set(list(range(self.size)))
self.updateA()
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 is not None:
kwargs['basis_vectors'] = basis_vectors
self.svdad = adapter.createSVDAdapter(**kwargs)
self.svals = np.mat(self.svdad.svals)
self.svecs = np.mat(self.svdad.rsvecs)
self.callbackfun = callback
self.regparam = regparam
self.constraint = 0
self.labelcount = int(number_of_clusters)
if self.labelcount == 2:
self.oneclass = True
else:
self.oneclass = False
if Y is not 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 is 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.lockvec = np.zeros((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 is not None:
self.fixedindices = fixed_indices
else:
self.fixedindices = []
self.results = {}
self.solve(self.regparam)
