def __init__(self, design, voi='pe', **kwargs): """ Parameters ---------- design : array (nsamples x nregressors) GLM design matrix. voi : {'pe', 'zstat'} Variable of interest that should be reported as feature-wise measure. 'beta' are the parameter estimates and 'zstat' returns standardized parameter estimates. """ FeaturewiseMeasure.__init__(self, **kwargs) # store the design matrix as a such (no copying if already array) self._design = np.asmatrix(design) # what should be computed ('variable of interest') if not voi in ['pe', 'zstat']: raise ValueError, \ "Unknown variable of interest '%s'" % str(voi) self._voi = voi # will store the precomputed Moore-Penrose pseudo-inverse of the # design matrix (lazy calculation) self._inv_design = None # also store the inverse of the inner product for beta variance # estimation self._inv_ip = None
def __init__(self, space="targets", **kwargs): """ Parameters ---------- space : str What samples attribute to use as targets (labels). """ # set auto-train flag since we have nothing special to be done FeaturewiseMeasure.__init__(self, auto_train=True, space=space, **kwargs)
def __init__(self, num_permutations=200, num_bootstraps=100, **kwargs): raise NotImplemented, 'PLS was not yet implemented fully' # init base classes first FeaturewiseMeasure.__init__(self, **kwargs) # save the args for the analysis self.num_permutations = num_permutations self.num_bootstraps = num_bootstraps
def __init__(self, space='targets', **kwargs): """ Parameters ---------- space : str What samples attribute to use as targets (labels). """ # set auto-train flag since we have nothing special to be done # so by default auto train kwargs['auto_train'] = kwargs.get('auto_train', True) FeaturewiseMeasure.__init__(self, space=space, **kwargs)
def __init__(self, attr='targets', **kwargs): """Initialize Parameters ---------- attr : str Attribute to correlate across chunks. """ # init base classes first FeaturewiseMeasure.__init__(self, **kwargs) self.__attr = attr
def __init__(self, threshold=1.0e-2, kernel_width=1.0, w_guess=None, **kwargs): """Constructor of the IRELIEF class. """ # init base classes first FeaturewiseMeasure.__init__(self, **kwargs) # Threshold in W changes (stopping criterion for irelief). self.threshold = threshold self.w_guess = w_guess self.w = None self.kernel_width = kernel_width
def __init__(self, pvalue=False, attr='targets', **kwargs): """Initialize Parameters ---------- pvalue : bool Either to report p-value of pearsons correlation coefficient instead of pure correlation coefficient attr : str What attribut to correlate with """ # init base classes first FeaturewiseMeasure.__init__(self, **kwargs) self.__pvalue = int(pvalue) self.__attr = attr
def __init__(self, datameasure, noise=np.random.normal): """ Parameters ---------- datameasure : `Measure` Used to quantify the effect of noise perturbation. noise: Callable Used to generate noise. The noise generator has to return an 1d array of n values when called the `size=n` keyword argument. This is the default interface of the random number generators in NumPy's `random` module. """ # init base classes first FeaturewiseMeasure.__init__(self) self.__datameasure = datameasure self.__noise = noise
def __init__(self, mult=1, **kwargs): FeaturewiseMeasure.__init__(self, **kwargs) self.__mult = mult