35c1805993c33e67a394193c7cae9a3f38a7607f,nilearn/decomposition/canica.py,CanICA,fit,#CanICA#Any#Any#Any#,134

Before Change

        random_state = check_random_state(self.random_state)

        sparsity = np.infty
        for rs in range(self.n_init):
            if (distutils.version.LooseVersion(sklearn.__version__).version
                    > [0, 12]):
                // random_state in fastica was added in 0.13
                ica_maps_ = self._cache(fastica, memory_level=6)(
                    self.components_.T,
                    whiten=True, fun="cube",
                    random_state=random_state)[2]
            else:
                ica_maps_ = self._cache(fastica, memory_level=6)(
                    self.components_.T, whiten=True,
                    fun="cube")[2]
            ica_maps_ = ica_maps_.T

            sparsity_ = np.sum(np.abs(ica_maps_), axis=1).max()
            if sparsity_ < sparsity:
                sparsity = sparsity_
                ica_maps = ica_maps_

        // Thresholding
        ratio = None
        if isinstance(self.threshold, float):
            ratio = self.threshold
        elif self.threshold == "auto":

After Change

        MultiPCA.fit(self, niimgs, y=y, confounds=confounds)
        random_state = check_random_state(self.random_state)

        seeds = random_state.randint(np.iinfo(np.int32).max, size=self.n_init)
        if (LooseVersion(sklearn.__version__).version > [0, 12]):
            // random_state in fastica was added in 0.13
            results = Parallel(n_jobs=self.n_jobs, verbose=self.verbose)(
                delayed(fastica)(self.components_.T,
                    whiten=True, fun="cube", random_state=seed)
                for seed in seeds)
        else:
            results = Parallel(n_jobs=1, verbose=self.verbose)(
                delayed(fastica)(self.components_.T, whiten=True, fun="cube")
                for seed in seeds)

Instances