class PLDA(object):
def __init__(self):
self._instance = MPlda()
def fit(self, x, y, iters=10):
return self._instance.fit(x, y, iters)
def transform(self, x, y):
'''
Function: transform
Summary: Transforms a given set of X vectors and Y labels to the PLDA dimension
Examples:
Attributes:
@param (self):
@param (x):The vectors which need to be transformed
@param (y):Representing the labels for each vector
Returns: A transformed vector
'''
return self._instance.transform(x, y)
def norm(self, vectors, transformedvecs, numutts=0):
'''
Function: norm
Summary: Normalizes the given model with mean/variance estimators
Examples:
Attributes:
@param (self):
@param (vectors):The input vectors for accumulating the statistics
@param (transformedvecs):The enrol models which are scored against
@param (numutts) default=0: Number of utterances which are considered
Returns: None
'''
return self._instance.norm(vectors, transformedvecs, numutts)
def score(self, target, xvec, yvec):
'''
Function: score
Summary: Scores a given target against a given x,y vector pair
Examples:
Attributes:
@param (self):InsertHere
@param (target):The id of the fitted target vector
@param (xvec):The enrolment vector
@param (yvec):The test vector
Returns: A score ( float)
'''
return self._instance.score(target, xvec, yvec)
def __init__(self):
self._instance = MPlda()