288 lines
10 KiB
Python
288 lines
10 KiB
Python
# Author: Hamzeh Alsalhi <ha258@cornell.edu>
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#
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# License: BSD 3 clause
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from __future__ import division
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import numpy as np
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import scipy.sparse as sp
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import operator
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import array
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from sklearn.utils import check_random_state
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from ._random import sample_without_replacement
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__all__ = ['sample_without_replacement', 'choice']
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# This is a backport of np.random.choice from numpy 1.7
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# The function can be removed when we bump the requirements to >=1.7
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def choice(a, size=None, replace=True, p=None, random_state=None):
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"""
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choice(a, size=None, replace=True, p=None)
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Generates a random sample from a given 1-D array
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.. versionadded:: 1.7.0
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Parameters
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-----------
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a : 1-D array-like or int
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If an ndarray, a random sample is generated from its elements.
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If an int, the random sample is generated as if a was np.arange(n)
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size : int or tuple of ints, optional
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Output shape. Default is None, in which case a single value is
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returned.
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replace : boolean, optional
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Whether the sample is with or without replacement.
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p : 1-D array-like, optional
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The probabilities associated with each entry in a.
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If not given the sample assumes a uniform distribtion over all
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entries in a.
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random_state : int, RandomState instance or None, optional (default=None)
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If int, random_state is the seed used by the random number generator;
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If RandomState instance, random_state is the random number generator;
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If None, the random number generator is the RandomState instance used
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by `np.random`.
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Returns
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--------
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samples : 1-D ndarray, shape (size,)
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The generated random samples
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Raises
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-------
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ValueError
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If a is an int and less than zero, if a or p are not 1-dimensional,
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if a is an array-like of size 0, if p is not a vector of
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probabilities, if a and p have different lengths, or if
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replace=False and the sample size is greater than the population
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size
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See Also
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---------
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randint, shuffle, permutation
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Examples
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---------
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Generate a uniform random sample from np.arange(5) of size 3:
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>>> np.random.choice(5, 3) # doctest: +SKIP
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array([0, 3, 4])
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>>> #This is equivalent to np.random.randint(0,5,3)
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Generate a non-uniform random sample from np.arange(5) of size 3:
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>>> np.random.choice(5, 3, p=[0.1, 0, 0.3, 0.6, 0]) # doctest: +SKIP
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array([3, 3, 0])
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Generate a uniform random sample from np.arange(5) of size 3 without
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replacement:
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>>> np.random.choice(5, 3, replace=False) # doctest: +SKIP
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array([3,1,0])
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>>> #This is equivalent to np.random.shuffle(np.arange(5))[:3]
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Generate a non-uniform random sample from np.arange(5) of size
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3 without replacement:
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>>> np.random.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0])
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... # doctest: +SKIP
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array([2, 3, 0])
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Any of the above can be repeated with an arbitrary array-like
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instead of just integers. For instance:
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>>> aa_milne_arr = ['pooh', 'rabbit', 'piglet', 'Christopher']
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>>> np.random.choice(aa_milne_arr, 5, p=[0.5, 0.1, 0.1, 0.3])
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... # doctest: +SKIP
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array(['pooh', 'pooh', 'pooh', 'Christopher', 'piglet'],
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dtype='|S11')
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"""
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random_state = check_random_state(random_state)
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# Format and Verify input
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a = np.array(a, copy=False)
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if a.ndim == 0:
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try:
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# __index__ must return an integer by python rules.
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pop_size = operator.index(a.item())
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except TypeError:
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raise ValueError("a must be 1-dimensional or an integer")
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if pop_size <= 0:
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raise ValueError("a must be greater than 0")
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elif a.ndim != 1:
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raise ValueError("a must be 1-dimensional")
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else:
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pop_size = a.shape[0]
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if pop_size is 0:
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raise ValueError("a must be non-empty")
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if None != p:
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p = np.array(p, dtype=np.double, ndmin=1, copy=False)
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if p.ndim != 1:
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raise ValueError("p must be 1-dimensional")
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if p.size != pop_size:
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raise ValueError("a and p must have same size")
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if np.any(p < 0):
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raise ValueError("probabilities are not non-negative")
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if not np.allclose(p.sum(), 1):
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raise ValueError("probabilities do not sum to 1")
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shape = size
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if shape is not None:
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size = np.prod(shape, dtype=np.intp)
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else:
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size = 1
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# Actual sampling
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if replace:
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if None != p:
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cdf = p.cumsum()
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cdf /= cdf[-1]
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uniform_samples = random_state.random_sample(shape)
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idx = cdf.searchsorted(uniform_samples, side='right')
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# searchsorted returns a scalar
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idx = np.array(idx, copy=False)
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else:
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idx = random_state.randint(0, pop_size, size=shape)
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else:
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if size > pop_size:
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raise ValueError("Cannot take a larger sample than "
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"population when 'replace=False'")
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if None != p:
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if np.sum(p > 0) < size:
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raise ValueError("Fewer non-zero entries in p than size")
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n_uniq = 0
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p = p.copy()
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found = np.zeros(shape, dtype=np.int)
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flat_found = found.ravel()
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while n_uniq < size:
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x = random_state.rand(size - n_uniq)
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if n_uniq > 0:
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p[flat_found[0:n_uniq]] = 0
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cdf = np.cumsum(p)
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cdf /= cdf[-1]
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new = cdf.searchsorted(x, side='right')
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_, unique_indices = np.unique(new, return_index=True)
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unique_indices.sort()
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new = new.take(unique_indices)
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flat_found[n_uniq:n_uniq + new.size] = new
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n_uniq += new.size
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idx = found
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else:
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idx = random_state.permutation(pop_size)[:size]
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if shape is not None:
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idx.shape = shape
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if shape is None and isinstance(idx, np.ndarray):
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# In most cases a scalar will have been made an array
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idx = idx.item(0)
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# Use samples as indices for a if a is array-like
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if a.ndim == 0:
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return idx
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if shape is not None and idx.ndim == 0:
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# If size == () then the user requested a 0-d array as opposed to
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# a scalar object when size is None. However a[idx] is always a
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# scalar and not an array. So this makes sure the result is an
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# array, taking into account that np.array(item) may not work
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# for object arrays.
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res = np.empty((), dtype=a.dtype)
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res[()] = a[idx]
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return res
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return a[idx]
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def random_choice_csc(n_samples, classes, class_probability=None,
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random_state=None):
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"""Generate a sparse random matrix given column class distributions
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Parameters
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----------
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n_samples : int,
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Number of samples to draw in each column.
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classes : list of size n_outputs of arrays of size (n_classes,)
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List of classes for each column.
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class_probability : list of size n_outputs of arrays of size (n_classes,)
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Optional (default=None). Class distribution of each column. If None the
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uniform distribution is assumed.
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random_state : int, RandomState instance or None, optional (default=None)
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If int, random_state is the seed used by the random number generator;
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If RandomState instance, random_state is the random number generator;
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If None, the random number generator is the RandomState instance used
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by `np.random`.
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Return
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------
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random_matrix : sparse csc matrix of size (n_samples, n_outputs)
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"""
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data = array.array('i')
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indices = array.array('i')
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indptr = array.array('i', [0])
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for j in range(len(classes)):
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classes[j] = np.asarray(classes[j])
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if classes[j].dtype.kind != 'i':
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raise ValueError("class dtype %s is not supported" %
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classes[j].dtype)
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classes[j] = classes[j].astype(int)
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# use uniform distribution if no class_probability is given
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if class_probability is None:
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class_prob_j = np.empty(shape=classes[j].shape[0])
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class_prob_j.fill(1 / classes[j].shape[0])
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else:
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class_prob_j = np.asarray(class_probability[j])
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if np.sum(class_prob_j) != 1.0:
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raise ValueError("Probability array at index {0} does not sum to "
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"one".format(j))
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if class_prob_j.shape[0] != classes[j].shape[0]:
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raise ValueError("classes[{0}] (length {1}) and "
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"class_probability[{0}] (length {2}) have "
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"different length.".format(j,
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classes[j].shape[0],
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class_prob_j.shape[0]))
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# If 0 is not present in the classes insert it with a probability 0.0
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if 0 not in classes[j]:
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classes[j] = np.insert(classes[j], 0, 0)
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class_prob_j = np.insert(class_prob_j, 0, 0.0)
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# If there are nonzero classes choose randomly using class_probability
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if classes[j].shape[0] > 1:
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p_nonzero = 1 - class_prob_j[classes[j] == 0]
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nnz = int(n_samples * p_nonzero)
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ind_sample = sample_without_replacement(n_population=n_samples,
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n_samples=nnz,
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random_state=random_state)
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indices.extend(ind_sample)
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# Normalize probabilites for the nonzero elements
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classes_j_nonzero = classes[j] != 0
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class_probability_nz = class_prob_j[classes_j_nonzero]
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class_probability_nz_norm = (class_probability_nz /
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np.sum(class_probability_nz))
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classes_ind = np.searchsorted(class_probability_nz_norm.cumsum(),
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np.random.rand(nnz))
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data.extend(classes[j][classes_j_nonzero][classes_ind])
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indptr.append(len(indices))
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return sp.csc_matrix((data, indices, indptr),
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(n_samples, len(classes)),
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dtype=int)
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