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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def fit(self, X, y=None):
 """Learn a segmentation on training data Fits event patterns and a segmentation to training data. After running this function, the learned event patterns can be used to segment other datasets using find_events Parameters X: time by voxel ndarray, or a list of such ndarrays fMRI data to be segmented. If a list is given, then all datasets are segmented simultaneously with the same event patterns y: not used (added to comply with BaseEstimator definition) Returns ------- self: the EventSegment object """


        X = copy.deepcopy(X)
        if type(X) is not list:
            X = check_array(X)
            X = [X]

        n_train = len(X)
        for i in range(n_train):
            X[i] = X[i].T

        self.classes_ = np.arange(self.n_events)
        n_dim = X[0].shape[0]
        for i in range(n_train):
            assert (X[i].shape[0] == n_dim)

        # Double-check that data is z-scored in time
        for i in range(n_train):
            X[i] = stats.zscore(X[i], axis=1, ddof=1)

        # Initialize variables for fitting
        log_gamma = []
        for i in range(n_train):
            log_gamma.append(np.zeros((X[i].shape[1],
                                       self.n_events)))
        step = 1
        best_ll = float("-inf")
        self.ll_ = np.empty((0, n_train))
        while step <= self.n_iter:
            iteration_var = self.step_var(step)

            # Based on the current segmentation, compute the mean pattern
            # for each event
            seg_prob = [np.exp(lg) / np.sum(np.exp(lg), axis=0)
                        for lg in log_gamma]
            mean_pat = np.empty((n_train, n_dim, self.n_events))
            for i in range(n_train):
                mean_pat[i, :, :] = X[i].dot(seg_prob[i])
            mean_pat = np.mean(mean_pat, axis=0)

            # Based on the current mean patterns, compute the event
            # segmentation
            self.ll_ = np.append(self.ll_, np.empty((1, n_train)), axis=0)
            for i in range(n_train):
                logprob = self._logprob_obs(X[i],
                                            mean_pat, iteration_var)
                log_gamma[i], self.ll_[-1, i] = self._forward_backward(logprob)

            # If log-likelihood has started decreasing, undo last step and stop
            if np.mean(self.ll_[-1, :]) < best_ll:
                self.ll_ = self.ll_[:-1, :]
                break

            self.segments_ = [np.exp(lg) for lg in log_gamma]
            self.event_var_ = iteration_var
            self.event_pat_ = mean_pat
            best_ll = np.mean(self.ll_[-1, :])
            logger.debug("Fitting step %d, LL=%f", step, best_ll)

            step += 1

        return self