amueller

from sklearn.datasets import load_digits
from sklearn.svm import SVC
from sklearn.utils import shuffle
from sklearn.metrics import zero_one_score
import numpy as np


digits = load_digits()
X, y = shuffle(digits.data, digits.target)
X_train, X_test = X[:1000, :], X[1000:, :]

amueller

from sklearn import datasets, manifold
from sklearn.neighbors import NearestNeighbors
import numpy as np


n_points = 1000
n_neighbors = 10
out_dim = 2 

n_trials = 100

amueller

import numpy as np

from IPython.parallel import Client

from sklearn.grid_search import GridSearchCV
from sklearn.cross_validation import KFold
from sklearn.svm import SVC
from sklearn import datasets
from sklearn.preprocessing import Scaler
from sklearn.utils import shuffle

amueller

import numpy as np
import scipy.stats


class ChineseRestaurantProcess(object):
    def __init__(self, alpha):
        self.alpha = alpha
        self.customers = []
    def sample(self, n_samples=1):
        samples = []

amueller

import numpy as np
import warnings

from itertools import cycle, izip

from sklearn.utils import gen_even_slices
from sklearn.utils import shuffle
from sklearn.base import BaseEstimator
from sklearn.base import ClassifierMixin
from sklearn.preprocessing import LabelBinarizer

amueller

import numpy as np

from sklearn import datasets
from sklearn.cross_validation import ShuffleSplit
from sklearn.grid_search import GridSearchCV
from sklearn.svm import SVC
from sklearn.preprocessing import Scaler

#data = datasets.load_digits()
data = datasets.fetch_mldata("usps")

amueller

import numpy as np
import matplotlib.pyplot as plt

from sklearn.svm import LinearSVC
from sklearn.cross_validation import ShuffleSplit
from sklearn.grid_search import GridSearchCV

from sklearn import datasets

n_samples = 100

amueller

import numpy as np

from sklearn.grid_search import GridSearchCV
from sklearn.cross_validation import StratifiedKFold
from sklearn.datasets import load_iris
from sklearn.svm import LinearSVC

iris = load_iris()
X, y = iris.data, iris.target
cv = StratifiedKFold(y, 3)

amueller

import numpy as np
import matplotlib.pyplot as plt

from sklearn.datasets import fetch_mldata
from sklearn.decomposition import FastICA, PCA
from sklearn.cluster import KMeans

# fetch natural image patches
image_patches = fetch_mldata("natural scenes data")
X = image_patches.data

amueller

from sklearn.grid_search import GridSearchCV
from sklearn.cross_validation import StratifiedKFold

def main():
    mnist = fetch_mldata("MNIST original")
    X_all, y_all = mnist.data/255., mnist.target
    print("scaling")
    X = X_all[:60000, :]
    y = y_all[:60000]