In [1]:
import sys
from time import time
from pprint import pprint
import numpy as np
import scipy
import scipy.sparse as sp
import joblib
import io
import os.path
import sklearn
import sklearn.svm
import sklearn.datasets
import sklearn.metrics
import sklearn.cross_validation
from sklearn.externals.six import u, b
from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction.text import HashingVectorizer
from sklearn.svm import LinearSVC
from sklearn.feature_selection import SelectKBest, chi2
from svmlight_loader import (load_svmlight_file, load_svmlight_files,dump_svmlight_file)
from sklearn.datasets import load_svmlight_file as sk_load_svmlight_file
from sklearn import decomposition
from scikits.learn.decomposition import NMF
import warnings
warnings.filterwarnings('ignore')
%pylab inline
In [2]:
X_train, y_train, X_test, y_test = load_svmlight_files(["svm.train.in", "svm.test.in"],dtype=np.float32)
print X_train.shape
print y_train.shape
print X_test.shape
print X_test.shape
print X_train.dtype
#print X_train[:1]
In [3]:
X = np.zeros(X_train.shape)
for x, y in np.ndindex(X_train.shape):
X[x,y]=X_train[x,y]
In [4]:
print X.shape
print X[:1]
print np.max(X)
print np.min(X)
In [ ]:
model = NMF(n_components=10,init='nndsvd')
model.fit(X)
print model.components_
print model.reconstruction_err_
In [18]:
print model.components_.shape
In [ ]:
print model.