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Add brute KNN
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@ -15,7 +15,6 @@
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"""
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kNN-CDF calculation
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"""
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from gc import collect
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import numpy
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from scipy.interpolate import interp1d
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from scipy.stats import binned_statistic
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@ -125,6 +124,55 @@ class kNN_CDF:
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cdf[cdf > 0.5] = 1 - cdf[cdf > 0.5]
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return cdf
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def brute_cdf(self, knn, nneighbours, Rmax, nsamples, rmin, rmax, neval,
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random_state=42, dtype=numpy.float32):
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"""
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Calculate the CDF for a kNN of CSiBORG halo catalogues without batch
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sizing. This can become memory intense for large numbers of randoms
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and, therefore, is only for testing purposes.
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Parameters
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----------
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knns : `sklearn.neighbors.NearestNeighbors`
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kNN of CSiBORG halo catalogues.
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neighbours : int
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Maximum number of neighbours to use for the kNN-CDF calculation.
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Rmax : float
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Maximum radius of the sphere in which to sample random points for
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the knn-CDF calculation. This should match the CSiBORG catalogues.
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nsamples : int
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Number of random points to sample for the knn-CDF calculation.
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rmin : float
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Minimum distance to evaluate the CDF.
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rmax : float
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Maximum distance to evaluate the CDF.
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neval : int
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Number of points to evaluate the CDF.
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random_state : int, optional
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Random state for the random number generator.
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dtype : numpy dtype, optional
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Calculation data type. By default `numpy.float32`.
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Returns
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-------
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rs : 1-dimensional array
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Distances at which the CDF is evaluated.
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cdfs : 2-dimensional array
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CDFs evaluated at `rs`.
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"""
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rand = self.rvs_in_sphere(nsamples, Rmax, random_state=random_state)
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dist, __ = knn.kneighbors(rand, nneighbours)
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dist = dist.astype(dtype)
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cdf = [None] * nneighbours
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for j in range(nneighbours):
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rs, cdf[j] = self.cdf_from_samples(dist[:, j], rmin=rmin,
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rmax=rmax, neval=neval)
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cdf = numpy.asanyarray(cdf)
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return rs, cdf
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def __call__(self, *knns, nneighbours, Rmax, nsamples, rmin, rmax, neval,
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batch_size=None, verbose=True, random_state=42,
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left_nan=True, right_nan=True, dtype=numpy.float32):
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