Python numpy.zeroes() Examples

def batch_update(self, mini_batch, eta, n, regularization=L2):
		""" Update the network's weights and biases by applying gradient
		descent using backpropagation to a single mini batch. """
		nabla_b = [np.zeroes(b.shape) for b in self.biases]
		nabla_w = [np.zeros(w.shape) for w in self.weights]
		for x, y in mini_batch:
			delta_nabla_b, delta_nabla_w = self.back_propogation(x, y)
			nabla_b = [nb+dnb for nb, dnb in zip(nabla_b, delta_nabla_b)]
			nabla_w = [nw+dnw for nw, dnw in zip(nabla_w, delta_nabla_w)]
		self.biases = [b-(eta/len(mini_batch))*nb for b, nb in zip(self.biases, nabla_b)]
		if regularization == L2:
			self.weights = [(1-eta*(self.l2/n))*w-(eta/len(mini_batch))*nw for w, nw in zip(self.weights, nabla_w)]
		elif regularization == L1:
			self.weights = [w - eta*self.l1*np.sign(w)/n-(eta/len(mini_batch))*nw for w, nw in zip(self.weights, nabla_w)] 

def _make_test_folds(self, X, y=None):
        rng = check_random_state(self.random_state)
        y = np.asarray(y)
        type_of_target_y = type_of_target(y)
        allowed_target_types = ('binary', 'multiclass')
        if type_of_target_y not in allowed_target_types:
            raise ValueError(
                'Supported target types are: {}. Got {!r} instead.'.format(
                    allowed_target_types, type_of_target_y))

        y = column_or_1d(y)
        n_samples = y.shape[0]
        unique_y, y_inversed = np.unique(y, return_inverse=True)
        y_counts = np.bincount(y_inversed)
        min_groups = np.min(y_counts)
        if np.all(self.n_splits > y_counts):
            raise ValueError("n_splits=%d cannot be greater than the"
                             " number of members in each class."
                             % (self.n_splits))
        if self.n_splits > min_groups:
            warnings.warn(("The least populated class in y has only %d"
                           " members, which is too few. The minimum"
                           " number of members in any class cannot"
                           " be less than n_splits=%d."
                           % (min_groups, self.n_splits)), Warning)

        # pre-assign each sample to a test fold index using individual KFold
        # splitting strategies for each class so as to respect the balance of
        # classes
        # NOTE: Passing the data corresponding to ith class say X[y==class_i]
        # will break when the data is not 100% stratifiable for all classes.
        # So we pass np.zeroes(max(c, n_splits)) as data to the KFold
        per_cls_cvs = [
            KFold(self.n_splits, shuffle=self.shuffle,
                  random_state=rng).split(np.zeros(max(count, self.n_splits)))
            for count in y_counts]

        test_folds = np.zeros(n_samples, dtype=np.int)
        for test_fold_indices, per_cls_splits in enumerate(zip(*per_cls_cvs)):
            for cls, (_, test_split) in zip(unique_y, per_cls_splits):
                cls_test_folds = test_folds[y == cls]
                # the test split can be too big because we used
                # KFold(...).split(X[:max(c, n_splits)]) when data is not 100%
                # stratifiable for all the classes
                # (we use a warning instead of raising an exception)
                # If this is the case, let's trim it:
                test_split = test_split[test_split < len(cls_test_folds)]
                cls_test_folds[test_split] = test_fold_indices
                test_folds[y == cls] = cls_test_folds

        return test_folds 

def _make_test_folds(self, X, y=None):
        rng = self.random_state
        y = np.asarray(y)
        n_samples = y.shape[0]
        unique_y, y_inversed = np.unique(y, return_inverse=True)
        y_counts = np.bincount(y_inversed)
        min_groups = np.min(y_counts)
        if np.all(self.n_splits > y_counts):
            raise ValueError("n_splits=%d cannot be greater than the"
                             " number of members in each class."
                             % (self.n_splits))
        if self.n_splits > min_groups:
            warnings.warn(("The least populated class in y has only %d"
                           " members, which is too few. The minimum"
                           " number of members in any class cannot"
                           " be less than n_splits=%d."
                           % (min_groups, self.n_splits)), Warning)

        # pre-assign each sample to a test fold index using individual KFold
        # splitting strategies for each class so as to respect the balance of
        # classes
        # NOTE: Passing the data corresponding to ith class say X[y==class_i]
        # will break when the data is not 100% stratifiable for all classes.
        # So we pass np.zeroes(max(c, n_splits)) as data to the KFold
        per_cls_cvs = [
            KFold(self.n_splits, shuffle=self.shuffle,
                  random_state=rng).split(np.zeros(max(count, self.n_splits)))
            for count in y_counts]

        test_folds = np.zeros(n_samples, dtype=np.int)
        for test_fold_indices, per_cls_splits in enumerate(zip(*per_cls_cvs)):
            for cls, (_, test_split) in zip(unique_y, per_cls_splits):
                cls_test_folds = test_folds[y == cls]
                # the test split can be too big because we used
                # KFold(...).split(X[:max(c, n_splits)]) when data is not 100%
                # stratifiable for all the classes
                # (we use a warning instead of raising an exception)
                # If this is the case, let's trim it:
                test_split = test_split[test_split < len(cls_test_folds)]
                cls_test_folds[test_split] = test_fold_indices
                test_folds[y == cls] = cls_test_folds

        return test_folds 

def zscore(a, axis=0, ddof=0):
        """ A static method to return the normalised version of series.
        This mirrors the scipy implementation
        with a small difference - rather than allowing /0, the function
        returns output = np.zeroes(len(input)).
        This is to allow for sensible processing of candidate
        shapelets/comparison subseries that are a straight
        line. Original version:
        https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats
        .zscore.html

        Parameters
        ----------
        a : array_like
            An array like object containing the sample data.

        axis : int or None, optional
            Axis along which to operate. Default is 0. If None, compute over
            the whole array a.

        ddof : int, optional
            Degrees of freedom correction in the calculation of the standard
            deviation. Default is 0.

        Returns
        -------
        zscore : array_like
            The z-scores, standardized by mean and standard deviation of
            input array a.
        """
        zscored = np.empty(a.shape)
        for i, j in enumerate(a):
            # j = np.asanyarray(j)
            sstd = j.std(axis=axis, ddof=ddof)

            # special case - if shapelet is a straight line (i.e. no
            # variance), zscore ver should be np.zeros(len(a))
            if sstd == 0:
                zscored[i] = np.zeros(len(j))
            else:
                mns = j.mean(axis=axis)
                if axis and mns.ndim < j.ndim:
                    zscored[i] = ((j - np.expand_dims(mns, axis=axis)) /
                                  np.expand_dims(sstd, axis=axis))
                else:
                    zscored[i] = (j - mns) / sstd
        return zscored 

def _make_test_folds(self, X, y=None):
        rng = self.random_state
        y = np.asarray(y)
        type_of_target_y = type_of_target(y)
        allowed_target_types = ('binary', 'multiclass')
        if type_of_target_y not in allowed_target_types:
            raise ValueError(
                'Supported target types are: {}. Got {!r} instead.'.format(
                    allowed_target_types, type_of_target_y))

        y = column_or_1d(y)
        n_samples = y.shape[0]
        unique_y, y_inversed = np.unique(y, return_inverse=True)
        y_counts = np.bincount(y_inversed)
        min_groups = np.min(y_counts)
        if np.all(self.n_splits > y_counts):
            raise ValueError("n_splits=%d cannot be greater than the"
                             " number of members in each class."
                             % (self.n_splits))
        if self.n_splits > min_groups:
            warnings.warn(("The least populated class in y has only %d"
                           " members, which is too few. The minimum"
                           " number of members in any class cannot"
                           " be less than n_splits=%d."
                           % (min_groups, self.n_splits)), Warning)

        # pre-assign each sample to a test fold index using individual KFold
        # splitting strategies for each class so as to respect the balance of
        # classes
        # NOTE: Passing the data corresponding to ith class say X[y==class_i]
        # will break when the data is not 100% stratifiable for all classes.
        # So we pass np.zeroes(max(c, n_splits)) as data to the KFold
        per_cls_cvs = [
            KFold(self.n_splits, shuffle=self.shuffle,
                  random_state=rng).split(np.zeros(max(count, self.n_splits)))
            for count in y_counts]

        test_folds = np.zeros(n_samples, dtype=np.int)
        for test_fold_indices, per_cls_splits in enumerate(zip(*per_cls_cvs)):
            for cls, (_, test_split) in zip(unique_y, per_cls_splits):
                cls_test_folds = test_folds[y == cls]
                # the test split can be too big because we used
                # KFold(...).split(X[:max(c, n_splits)]) when data is not 100%
                # stratifiable for all the classes
                # (we use a warning instead of raising an exception)
                # If this is the case, let's trim it:
                test_split = test_split[test_split < len(cls_test_folds)]
                cls_test_folds[test_split] = test_fold_indices
                test_folds[y == cls] = cls_test_folds

        return test_folds