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from sklearn.manifold import MDS
import numpy as np

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

def mat_usuarios ():


    a1=[5,2,1,5]
    a2=[4,5,2,3]
    a3=[2,5,5,1]
    a4=[5,3,1,5]
    a5=[3,4,5,2]
    a6=[1,4,3,5]
    usuarios=[a1,a2,a3,a4,a5,a6]
    mat_usuarios =[]

    for i in range (0,6):
        new=[]
        for x in range (0,6):
            new.append(0)

        mat_usuarios.append(new)



    for (i,x) in enumerate(usuarios):
        for (j,y) in enumerate(usuarios):
            dist=dist_vector(x,y)
            mat_usuarios[i][j]=dist

    print np.array(mat_usuarios)
    return np.array(mat_usuarios)

def mat_peliculas ():

    p1=[5,4,2,5,3,1]
    p2=[2,5,5,3,4,4]
    p3=[1,2,5,1,5,3]
    p4=[5,3,1,5,2,5]
    peliculas=[p1,p2,p3,p4]
    mat_peliculas=[]

    for i in range (0,4):
        new=[]
        for x in range (0,4):
            new.append(0)

        mat_peliculas.append(new)



    for (i,x) in enumerate(peliculas):
        for (j,y) in enumerate(peliculas):
            dist=dist_vector(x,y)
            mat_peliculas[i][j]=dist



    print np.array(mat_peliculas)
    return np.array(mat_peliculas)

def mds(mat):

    nmds = MDS(n_components=1,dissimilarity="precomputed")
    npos = nmds.fit_transform(mat)
    print npos

    plt.plot(npos, marker='o', linestyle='None')
    plt.xlim([-1,8])
    plt.ylim([-5,5])
    plt.show()


def dist_vector(x,y):
    total=0
    for i,j in zip(x,y):
        total+= (i-j)**2
    return total**0.5

def imprimir_matriz (mat):
    for x in mat:
        print x
mat=mat_usuarios()
mds(mat)