"""
=============
Multiview MDS
=============

An example plot of multiview MDS, using multiples views from same data thanks
to data given in `UCI Machine Learning Repository
<https://archive.ics.uci.edu/ml/datasets/Multiple+Features>`_.
"""
import numpy as np
from matplotlib import pyplot as plt

import os
import sys
sys.path.insert(0, os.path.abspath('..'))

from multiview_gpu.mvmds import MVMDS


def readData(filename, data_type=0):
    """
    Given a txt file, returns a numpy matrix with the values, according
    to datatype specified in data_type parameters.

    Parameters
    ----------
    filename: string
        Path or name of the txt file.
    data_type: integer, default 0
        Specifies the matrix datatype. If data_type is 0, data loaded will be
        float type. If data_type is 1, matrix datatype will be float.

    Returns
    -------
    output: ndarray
        Matrix with data loaded.
    """
    if data_type != 0 and data_type != 1:
        raise ValueError('data_type must be either 0 or 1. Found value %d '
                         'instead.' % data_type)
    with open(filename) as txtfile:

        result = []
        myreader = txtfile.readlines()

        for row in myreader:
            if data_type == 0:
                result.append([float(x) for x in row.split()])
            elif data_type == 1:
                result.append([int(x) for x in row.split()])
    if data_type == 0:
        return np.array(result)
    else:
        return np.array(result, dtype='int')


#####################################################

fourier = readData("mfeat-fou.txt", 0)
profcorr = readData("mfeat-fac.txt", 1)
pixels = readData("mfeat-pix.txt", 1)
morpho = readData("mfeat-mor.txt", 0)

markers = ['o', '2', '<', '*', 'h', 'x', 'D', '|', '_', 'v']
mypalette = ['green', 'purple', 'pink', 'blue', 'black',
             'brown', 'yellow', 'orange', 'gray', 'red']

is_distance = [False] * 4

mvmds = MVMDS(k=2)
projection = mvmds.fit_transform([fourier, profcorr, pixels, morpho],
                                 is_distance)
# print(projection)
for i in range(10):
    plt.scatter(projection[i * 200:200 * (i + 1), 0],
                projection[i * 200:200 * (1 + i), 1],
                c=mypalette[i], marker=markers[i])
plt.axis('off')
plt.show()