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src/s/c/scikit-learn-0.14.1/sklearn/datasets/lfw.py   scikit-learn(Download)
 
from .base import get_data_home, Bunch
from ..externals.joblib import Memory
 
from ..externals.six import b, u
    # wrap the loader in a memoizing function that will return memmaped data
    # arrays for optimal memory usage
    m = Memory(cachedir=lfw_home, compress=6, verbose=0)
    load_func = m.cache(_fetch_lfw_people)
 
    # wrap the loader in a memoizing function that will return memmaped data
    # arrays for optimal memory usage
    m = Memory(cachedir=lfw_home, compress=6, verbose=0)
    load_func = m.cache(_fetch_lfw_pairs)
 

src/s/c/scikit-learn-0.14.1/sklearn/cluster/hierarchical.py   scikit-learn(Download)
 
from ..base import BaseEstimator, ClusterMixin
from ..externals.joblib import Memory
from ..externals import six
from ..metrics import euclidean_distances
        X = array2d(X)
        if isinstance(memory, six.string_types):
            memory = Memory(cachedir=memory, verbose=0)
 
        if not self.connectivity is None:

src/s/c/scikits.learn-0.8.1/scikits/learn/cluster/hierarchical.py   scikits.learn(Download)
from ..base import BaseEstimator
from ..utils._csgraph import cs_graph_components
from ..externals.joblib import Memory
 
from . import _inertia
        memory = self.memory
        if isinstance(memory, basestring):
            memory = Memory(cachedir=memory)
 
        # Construct the tree