Generated by Cython 0.23.4
Yellow lines hint at Python interaction.
Click on a line that starts with a "+" to see the C code that Cython generated for it.
Raw output: kmeans.c
+001: #cython: boundscheck=False
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002: #cython: cdivision=True
003: # kmeans.pyx
004: # Contact: Jacob Schreiber ( jmschreiber91@gmail.com )
005:
006: from libc.stdlib cimport calloc
007: from libc.stdlib cimport free
008: from libc.string cimport memset
009: from libc.string cimport memcpy
010: from libc.math cimport log10 as clog10
011:
012: from .base cimport Model
013:
+014: import json
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+015: import numpy
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016: cimport numpy
017:
018:
019: DEF NEGINF = float("-inf")
020: DEF INF = float("inf")
021:
022:
+023: def initialize_centroids(numpy.ndarray X, numpy.ndarray weights, int k,
/* Python wrapper */
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static char __pyx_doc_11pomegranate_6kmeans_initialize_centroids[] = "Initialize the centroids for kmeans given a dataset.\n\n\tThis function will take in a dataset and return the centroids found using\n\tsome method. This is the initialization step of kmeans.\n\n\tParameters\n\t----------\n\tX : numpy.ndarray, shape=(n_samples, n_dim)\n\t\tThe dataset to identify centroids in.\n\n\tweights : numpy.ndarray, shape=(n_samples,)\n\t\tThe weights associated with each of the samples.\n\n\tk : int\n\t\tThe number of centroids to extract.\n\n\tinit : str, one of 'first-k', 'random', 'kmeans++'\n\t\t'first-k' : use the first k samples as the centroids\n\t\t'random' : randomly select k samples as the centroids\n\t\t'kmeans++' : use the kmeans++ initialization algorithm, which\n\t\t\titeratively selects the next centroid randomly, but weighted\n\t\t\tbased on distance to nearest centroid, to be likely to choose\n\t\t\tgood initializations\n\n\tReturns\n\t-------\n\n\t";
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024: init='first-k', double oversampling_factor=0.95):
025: """Initialize the centroids for kmeans given a dataset.
026:
027: This function will take in a dataset and return the centroids found using
028: some method. This is the initialization step of kmeans.
029:
030: Parameters
031: ----------
032: X : numpy.ndarray, shape=(n_samples, n_dim)
033: The dataset to identify centroids in.
034:
035: weights : numpy.ndarray, shape=(n_samples,)
036: The weights associated with each of the samples.
037:
038: k : int
039: The number of centroids to extract.
040:
041: init : str, one of 'first-k', 'random', 'kmeans++'
042: 'first-k' : use the first k samples as the centroids
043: 'random' : randomly select k samples as the centroids
044: 'kmeans++' : use the kmeans++ initialization algorithm, which
045: iteratively selects the next centroid randomly, but weighted
046: based on distance to nearest centroid, to be likely to choose
047: good initializations
048:
049: Returns
050: -------
051:
052: """
053:
+054: cdef int n = X.shape[0], d = X.shape[1]
__pyx_v_n = (__pyx_v_X->dimensions[0]); __pyx_v_d = (__pyx_v_X->dimensions[1]);
055: cdef int count, i, j, l, m
056: cdef double distance
057: cdef numpy.ndarray centroids
058: cdef numpy.ndarray min_distance
059: cdef numpy.ndarray min_idxs
060:
+061: cdef double* X_ptr = <double*> X.data
__pyx_v_X_ptr = ((double *)__pyx_v_X->data);
+062: cdef double* weights_ptr = <double*> weights.data
__pyx_v_weights_ptr = ((double *)__pyx_v_weights->data);
063: cdef double* min_distance_ptr
064: cdef double* centroids_ptr
065: cdef int* min_idxs_ptr
066: cdef double phi
067:
+068: weights = weights / weights.sum()
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069:
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+071: raise ValueError("initialization must be one of 'first-k', 'random', or 'kmeans++'")
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072:
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+074: centroids = X[:k].copy()
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246: """Fit the model to the data using k centroids.
247:
248: Parameters
249: ----------
250: X : array-like, shape (n_samples, n_dim)
251: The data to fit to.
252:
253: weights : array-like, shape (n_samples,), optional
254: The initial weights of each sample in the matrix. If nothing is
255: passed in then each sample is assumed to be the same weight.
256: Default is None.
257:
258: inertia : double, optional
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262: ignore the old parameters, whereas an inertia of 1 means ignore
263: the new parameters.
264: Default is 0.0.
265:
266: stop_threshold : double, optional, positive
267: The threshold at which EM will terminate for the improvement of
268: the model. If the model does not improve its fit of the data by a
269: log probability of 0.1 then terminate.
270: Default is 0.1.
271:
272: max_iterations : int, optional
273: The maximum number of iterations to run for. Default is 1e3.
274:
275: verbose : bool, optional
276: Whether or not to print out improvement information over
277: iterations.
278: Default is False.
279:
280: Returns
281: -------
282: None
283: """
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