22b6e2d4d19e8557980c5600c71676e6ceed4915,mvpa2/clfs/gnb.py,GNB,_train,#GNB#,144

Before Change

            self._norm_weight = 1.0/np.sqrt(2*np.pi*variances)

        // Add "has_sensitivity" tag if classifier is linear
        if params.common_variance \
            and "has_sensitivity" not in self.__tags__:
            self.__tags__ += ["has_sensitivity"]


        if __debug__ and "GNB" in debug.active:
            debug("GNB", "training finished on data.shape=%s " % (X.shape, )
                  + "min:max(data)=%f:%f" % (np.min(X), np.max(X)))

After Change

        else:
            self._norm_weight = 1.0/np.sqrt(2*np.pi*variances)

        assert params.common_variance == ("has_sensitivity" in self.__tags__)

        if __debug__ and "GNB" in debug.active:
            debug("GNB", "training finished on data.shape=%s " % (X.shape, )
                  + "min:max(data)=%f:%f" % (np.min(X), np.max(X)))

Instances