def apply_all_kernels(X, weights, lengths, biases, dilations, paddings):
    """Apply one kernel to a data set of time series.

    Parameters
    ----------
    X : array, shape = (n_samples, n_timestamps)
        Input data.

    weights : array, shape = (n_kernels, max(kernel_sizes))
        Weights of the kernels. Zero padding values are added.

    lengths : array, shape = (n_kernels,)
        Length of each kernel.

    biases : array, shape = (n_kernels,)
        Bias of each kernel.

    dilations : array, shape = (n_kernels,)
        Dilation of each kernel.

    paddings : array, shape = (n_kernels,)
        Padding of each kernel.

    Returns
    -------
    X_new : array, shape = (n_samples, 2 * n_kernels)
        Extracted features using all the kernels.

    """
    n_samples, n_timestamps = X.shape
    n_kernels = lengths.size
    X_new = np.empty((n_samples, 2 * n_kernels))
    for i in prange(n_samples):
        for j in prange(n_kernels):
            X_new[i, (2 * j):(2 * j + 2)] = apply_one_kernel_one_sample(
                X[i], n_timestamps, weights[j], lengths[j],
                biases[j], dilations[j], paddings[j]
            )
    return X_new
Apply one kernel to a data set of time series.

    Parameters
    ----------
    X : array, shape = (n_samples, n_timestamps)
        Input data.

    weights : array, shape = (n_kernels, max(kernel_sizes))
        Weights of the kernels. Zero padding values are added.

    lengths : array, shape = (n_kernels,)
        Length of each kernel.

    biases : array, shape = (n_kernels,)
        Bias of each kernel.

    dilations : array, shape = (n_kernels,)
        Dilation of each kernel.

    paddings : array, shape = (n_kernels,)
        Padding of each kernel.

    Returns
    -------
    X_new : array, shape = (n_samples, 2 * n_kernels)
        Extracted features using all the kernels.

    
pyts/transformation/rocket.py