kpysniak

import numpy as np
from sklearn import datasets
from sklearn.datasets import fetch_mldata
from sklearn.linear_model import SGDClassifier
from sklearn.linear_model.ridge import RidgeClassifier
from sklearn.linear_model.ridge import RidgeClassifierCV
from sklearn.multiclass import OneVsRestClassifier
from sklearn.multiclass import OneVsOneClassifier
from sklearn.svm import LinearSVC
from sklearn.linear_model.coordinate_descent import (Lasso, ElasticNet,

kpysniak

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import MiniBatchKMeans
from sklearn.feature_extraction.image import extract_patches_2d
from sklearn import datasets
import numpy as np
import time


def getKmeansFitter(kmeans_name):
    if kmeans_name == 'unique_labels':

kpysniak

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import MiniBatchKMeans
from sklearn.feature_extraction.image import extract_patches_2d
from sklearn import datasets
import numpy as np
import time

def calculate_image_inertia(faces, kmeans, patch_size):
    t0 = time.time()

kpysniak

from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import MiniBatchKMeans
import numpy as np


dataset = fetch_20newsgroups(subset='all',shuffle=True)

labels = dataset.target
true_k = np.unique(labels).shape[0]