amueller

amueller

# Standard scientific Python imports
import pylab as pl
import numpy as np
from time import time

# Import datasets, classifiers and performance metrics
from sklearn import datasets, svm, pipeline
from sklearn.kernel_approximation import (RBFSampler,
                                          Nystroem)
from sklearn.utils import shuffle

amueller

import numpy as np
import matplotlib.pyplot as plt
from itertools import product
from sklearn.decomposition import RandomizedPCA
from sklearn.datasets import fetch_mldata
from sklearn.utils import shuffle

mnist = fetch_mldata("MNIST original")
X_train, y_train = mnist.data[:60000] / 255., mnist.target[:60000]

amueller

amueller

  # Standard scientific Python imports
  import pylab as pl
  import numpy as np
  from time import time
      
  # Import datasets, classifiers and performance metrics
  from sklearn import datasets, svm, pipeline
  from sklearn.kernel_approximation import (RBFSampler,
                                            Nystroem)
  from sklearn.utils import shuffle

amueller

amueller

from sklearn.utils.testing import all_estimators
import numpy as np

all_objects = []
for C in all_estimators():
    try:
        all_objects.append(C[1]())
    except:
        pass
params = [c.get_params().keys() for c in all_objects]

amueller

# (c) 2012 Andreas Mueller [email protected]
# License: BSD 2-Clause
#
# See my blog for details: http://peekaboo-vision.blogspot.com

import numpy as np

import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation

amueller

blub

amueller

import numpy as np
from sklearn.feature_selection import chi2
data = np.load("values.npy")
labels = np.load("labels.npy")
print(chi2(data, labels))