"""
=======================================
Fit Hawkes kernel norms using cumulants
=======================================

This non parametric Hawkes cumulants matching
(`tick.hawkes.HawkesCumulantMatching`) algorithm estimates directly
kernels norms without making any assumption on kernel shapes.

It has been originally described in this paper:

Achab, M., Bacry, E., Gaiffas, S., Mastromatteo, I., & Muzy, J. F.
(2017, July). Uncovering causality from multivariate Hawkes integrated
cumulants.
`In International Conference on Machine Learning (pp. 1-10)`_.

.. _In International Conference on Machine Learning (pp. 1-10): http://proceedings.mlr.press/v70/achab17a.html
"""

skip = True
try:
    import tensorflow
    skip = False
except ImportError:
    print("tensorflow not found, skipping HawkesCumulantMatching")

if not skip:

    import numpy as np

    from tick.hawkes import (HawkesCumulantMatchingTf, SimuHawkesExpKernels,
                             SimuHawkesMulti)
    from tick.plot import plot_hawkes_kernel_norms

    np.random.seed(7168)

    n_nodes = 3
    baselines = 0.3 * np.ones(n_nodes)
    decays = 0.5 + np.random.rand(n_nodes, n_nodes)
    adjacency = np.array([
        [1, 1, -0.5],
        [0, 1, 0],
        [0, 0, 2],
    ], dtype=float)

    adjacency /= 4

    end_time = 1e5
    integration_support = 5
    n_realizations = 5

    simu_hawkes = SimuHawkesExpKernels(baseline=baselines, adjacency=adjacency,
                                       decays=decays, end_time=end_time,
                                       verbose=False, seed=7168)
    simu_hawkes.threshold_negative_intensity(True)

    multi = SimuHawkesMulti(simu_hawkes, n_simulations=n_realizations,
                            n_threads=-1)
    multi.simulate()

    nphc = HawkesCumulantMatchingTf(integration_support, cs_ratio=.15, tol=1e-10,
                                    step=0.3)

    nphc.fit(multi.timestamps)
    plot_hawkes_kernel_norms(nphc)