#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
compute gm with load_data.py and test them. 
Created on Wed Sep 19 16:12:13 2018

@author: ljia
"""

"""Shortest-Path graph kernel.
Python implementation based on: "Shortest-path kernels on graphs", by
Borgwardt, K.M.; Kriegel, H.-P., in Data Mining, Fifth IEEE
International Conference on , vol., no., pp.8 pp.-, 27-30 Nov. 2005
doi: 10.1109/ICDM.2005.132
Author : Sandro Vega-Pons, Emanuele Olivetti
"""

import sys
sys.path.insert(0, "../../")
import numpy as np
import networkx as nx
from gklearn.utils.graphfiles import loadDataset
import matplotlib.pyplot as plt
from numpy.linalg import eig


class GK_SP:
    """
    Shorthest path graph kernel.
    """

    def compare(self, g_1, g_2, verbose=False):
        """Compute the kernel value (similarity) between two graphs.
        Parameters
        ----------
        g1 : networkx.Graph
            First graph.
        g2 : networkx.Graph
            Second graph.
        Returns
        -------
        k : The similarity value between g1 and g2.
        """
        # Diagonal superior matrix of the floyd warshall shortest
        # paths:
        fwm1 = np.array(nx.floyd_warshall_numpy(g_1))
        fwm1 = np.where(fwm1 == np.inf, 0, fwm1)
        fwm1 = np.where(fwm1 == np.nan, 0, fwm1)
        fwm1 = np.triu(fwm1, k=1)
        bc1 = np.bincount(fwm1.reshape(-1).astype(int))

        fwm2 = np.array(nx.floyd_warshall_numpy(g_2))
        fwm2 = np.where(fwm2 == np.inf, 0, fwm2)
        fwm2 = np.where(fwm2 == np.nan, 0, fwm2)
        fwm2 = np.triu(fwm2, k=1)
        bc2 = np.bincount(fwm2.reshape(-1).astype(int))

        # Copy into arrays with the same length the non-zero shortests
        # paths:
        v1 = np.zeros(max(len(bc1), len(bc2)) - 1)
        v1[range(0, len(bc1)-1)] = bc1[1:]

        v2 = np.zeros(max(len(bc1), len(bc2)) - 1)
        v2[range(0, len(bc2)-1)] = bc2[1:]

        return np.sum(v1 * v2)

    def compare_normalized(self, g_1, g_2, verbose=False):
        """Compute the normalized kernel value between two graphs.
        A normalized version of the kernel is given by the equation:
        k_norm(g1, g2) = k(g1, g2) / sqrt(k(g1,g1) * k(g2,g2))
        Parameters
        ----------
        g1 : networkx.Graph
            First graph.
        g2 : networkx.Graph
            Second graph.
        Returns
        -------
        k : The similarity value between g1 and g2.
        """
        return self.compare(g_1, g_2) / (np.sqrt(self.compare(g_1, g_1) *
                                                 self.compare(g_2, g_2)))

    def compare_list(self, graph_list, verbose=False):
        """Compute the all-pairs kernel values for a list of graphs.
        This function can be used to directly compute the kernel
        matrix for a list of graphs. The direct computation of the
        kernel matrix is faster than the computation of all individual
        pairwise kernel values.
        Parameters
        ----------
        graph_list: list
            A list of graphs (list of networkx graphs)
        Return
        ------
        K: numpy.array, shape = (len(graph_list), len(graph_list))
        The similarity matrix of all graphs in graph_list.
        """
        n = len(graph_list)
        k = np.zeros((n, n))
        for i in range(n):
            for j in range(i, n):
                k[i, j] = self.compare(graph_list[i], graph_list[j])
                k[j, i] = k[i, j]

        k_norm = np.zeros(k.shape)
        for i in range(k.shape[0]):
            for j in range(k.shape[1]):
                k_norm[i, j] = k[i, j] / np.sqrt(k[i, i] * k[j, j])

        return k_norm


ds_name = 'PAH'
datafile = '../../datasets/PAH/dataset.ds'
dataset, y = loadDataset(datafile, filename_y=None, extra_params=None)
gk_sp = GK_SP()
x = gk_sp.compare_list(dataset)
np.savez('../check_gm/' + ds_name + '.gm.jstsp', gms=x)

plt.imshow(x)
plt.colorbar()
plt.savefig('../check_gm/' + ds_name + '.gm.jstsp.eps', format='eps', dpi=300)
# print(np.transpose(x))
print('if symmetric: ', np.array_equal(x, np.transpose(x)))

print('diag: ', np.diag(x))
print('sum diag < 0.1: ', np.sum(np.diag(x) < 0.1))
print('min, max diag: ', min(np.diag(x)), max(np.diag(x)))
print('mean x: ', np.mean(np.mean(x)))

[lamnda, v] = eig(x)
print('min, max lambda: ', min(lamnda), max(lamnda))