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steminc
steminc / scikit-learn python
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Version:
0.15.2 ▾
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ãþÿ|ãþÿsãþÿjãþÿaãþÿ²ãþÿÓãþÿôãþÿäþÿ7äþÿê#ÿÿÞ#ÿÿÒ#ÿÿ~#ÿÿu#ÿÿl#ÿÿc#ÿÿÆ#ÿÿ½#ÿÿ´#ÿÿ«#ÿÿ¢#ÿÿ#ÿÿ#ÿÿ#ÿÿZ#ÿÿQ#ÿÿ<#ÿÿneed more than %zd value%s to unpack %s() takes %s %zd positional argument%s (%zd given) Buffer dtype mismatch, expected %s%s%s but got %s Buffer dtype mismatch, expected '%s' but got %s in '%s.%s' %s() got multiple values for keyword argument '%s' %s() got an unexpected keyword argument '%s' __%s__ returned non-%s (type %.200s) memviewslice is already initialized! sklearn/neighbors/dist_metrics.c View.MemoryView._memoryviewslice.assign_item_from_object View.MemoryView._memoryviewslice.convert_item_to_object View.MemoryView.array.memview.__get__ View.MemoryView.memoryview.itemsize.__get__ View.MemoryView.memoryview.ndim.__get__ View.MemoryView.memoryview_cwrapper View.MemoryView.memoryview.setitem_indexed sklearn.neighbors.dist_metrics.DistanceMetric.__getstate__ sklearn.neighbors.dist_metrics.DistanceMetric.pdist Cannot convert %.200s to %.200s Expected a dimension of size %zu, got %zu Expected %d dimensions, got %d Unexpected format string character: '%c' Python does not define a standard format string size for long double ('g').. Buffer dtype mismatch; next field is at offset %zd but %zd expected Does not understand character buffer dtype format string ('%c') Big-endian buffer not supported on little-endian compiler Buffer acquisition: Expected '{' after 'T' Cannot handle repeated arrays in format string Expected a dimension of size %zu, got %d Expected a comma in format string, got '%c' Expected %d dimension(s), got %d Unexpected end of format string, expected ')' View.MemoryView.get_slice_from_memview sklearn.neighbors.dist_metrics.MahalanobisDistance.dist_to_rdist sklearn.neighbors.dist_metrics.MinkowskiDistance.dist_to_rdist sklearn.neighbors.dist_metrics.MinkowskiDistance.rdist_to_dist sklearn.neighbors.dist_metrics.MinkowskiDistance.dist sklearn.neighbors.dist_metrics.WMinkowskiDistance.dist_to_rdist sklearn.neighbors.dist_metrics.WMinkowskiDistance.rdist_to_dist sklearn.neighbors.dist_metrics.EuclideanDistance.dist_to_rdist sklearn.neighbors.dist_metrics.EuclideanDistance.rdist sklearn.neighbors.dist_metrics.EuclideanDistance.dist sklearn.neighbors.dist_metrics.SEuclideanDistance.dist_to_rdist sklearn.neighbors.dist_metrics.DistanceMetric.rdist %s.%s size changed, may indicate binary incompatibility %s.%s has the wrong size, try recompiling Acquisition count is %d (line %d) View.MemoryView.memoryview.shape.__get__ View.MemoryView.memoryview.suboffsets.__get__ View.MemoryView.array_cwrapper View.MemoryView.memoryview.is_slice sklearn.neighbors.dist_metrics._buffer_to_ndarray argument after ** must be a mapping, not NoneType sklearn.neighbors.dist_metrics.PyFuncDistance.dist View.MemoryView.array.__getitem__ View.MemoryView.array.__setitem__ Subscript deletion not supported by %s View.MemoryView.array.__getattr__ View.MemoryView.memoryview.nbytes.__get__ sklearn.neighbors.dist_metrics.newObj sklearn.neighbors.dist_metrics.SEuclideanDistance.rdist_to_dist sklearn.neighbors.dist_metrics.EuclideanDistance.rdist_to_dist sklearn.neighbors.dist_metrics.MahalanobisDistance.rdist_to_dist sklearn.neighbors.dist_metrics.DistanceMetric.__reduce__ View.MemoryView.memoryview.__str__ raise: arg 3 must be a traceback or None instance exception may not have a separate value raise: exception class must be a subclass of BaseException sklearn.neighbors.dist_metrics.DistanceMetric.cdist sklearn.neighbors.dist_metrics.DistanceMetric.__init__ integer division or modulo by zero value too large to perform division View.MemoryView.pybuffer_index View.MemoryView.memoryview.get_item_pointer View.MemoryView.memoryview.setitem_slice_assign_scalar View.MemoryView.assert_direct_dimensions View.MemoryView.transpose_memslice View.MemoryView.copy_data_to_temp View.MemoryView.memoryview_copy_contents Cannot copy memoryview slice with indirect dimensions (axis %d) View.MemoryView.memoryview.strides.__get__ sklearn.neighbors.dist_metrics.SEuclideanDistance.rdist sklearn.neighbors.dist_metrics.SEuclideanDistance.dist sklearn.neighbors.dist_metrics.WMinkowskiDistance.rdist sklearn.neighbors.dist_metrics.WMinkowskiDistance.dist sklearn.neighbors.dist_metrics.MahalanobisDistance.rdist sklearn.neighbors.dist_metrics.MahalanobisDistance.dist sklearn.neighbors.dist_metrics.HaversineDistance.dist sklearn.neighbors.dist_metrics.HaversineDistance.rdist sklearn.neighbors.dist_metrics.HaversineDistance.dist_to_rdist View.MemoryView.memoryview.__repr__ sklearn.neighbors.dist_metrics.MinkowskiDistance.__init__ sklearn.neighbors.dist_metrics.HaversineDistance.rdist_to_dist View.MemoryView.memoryview.convert_item_to_object View.MemoryView.memoryview_fromslice View.MemoryView.memoryview_copy_from_slice View.MemoryView.memoryview_copy View.MemoryView.memoryview.T.__get__ View.MemoryView.memoryview.copy_fortran View.MemoryView.memoryview.copy sklearn.neighbors.dist_metrics.PyFuncDistance.__init__ __float__ returned non-float (type %.200s) View.MemoryView.memoryview.assign_item_from_object View.MemoryView.slice_memviewslice local variable '%s' referenced before assignment value too large to convert to int View.MemoryView.memoryview.setitem_slice_assignment Argument '%s' has incorrect type (expected %s, got %s) View.MemoryView.array.__cinit__ Argument 'format' must not be None object of type 'NoneType' has no len() BufferFormatFromTypeInfo.format_from_typeinfo too many values to unpack (expected %zd) sklearn.neighbors.dist_metrics.get_valid_metric_ids View.MemoryView.memoryview.size.__get__ 'NoneType' object is not iterable View.MemoryView.array.__getbuffer__ '%100s' does not have the buffer interface View.MemoryView.memoryview.__cinit__ Buffer has wrong number of dimensions (expected %d, got %d) Item size of buffer (%zd byte%s) does not match size of '%s' (%zd byte%s) sklearn.neighbors.dist_metrics.get_vec_ptr sklearn.neighbors.dist_metrics.WMinkowskiDistance.__init__ sklearn.neighbors.dist_metrics.SEuclideanDistance.__init__ Cannot create cython.array from NULL pointer Item size of buffer (%zu byte%s) does not match size of '%s' (%zu byte%s) Buffer is not indirectly contiguous in dimension %d. Buffer and memoryview are not contiguous in the same dimension. C-contiguous buffer is not contiguous in dimension %d C-contiguous buffer is not indirect in dimension %d Buffer exposes suboffsets but no strides Buffer not compatible with direct access in dimension %d. Buffer is not indirectly accessisble in dimension %d. sklearn.neighbors.dist_metrics.get_memview_DTYPE_2D sklearn.neighbors.dist_metrics.DistanceMetric.pairwise View.MemoryView.memoryview.__setitem__ View.MemoryView.memoryview.__getitem__ sklearn.neighbors.dist_metrics.DistanceMetric.get_metric sklearn.neighbors.dist_metrics.get_mat_ptr sklearn.neighbors.dist_metrics.DistanceMetric.__cinit__ sklearn.neighbors.dist_metrics.MahalanobisDistance.__init__ sklearn.neighbors.dist_metrics.DistanceMetric.__setstate__ sklearn.neighbors.dist_metrics compiletime version %s of module '%.100s' does not match runtime version %s numpy.core.multiarray failed to import _ARRAY_API is not PyCObject object module compiled against ABI version 0x%x but this version of numpy is 0x%x module compiled against API version 0x%x but this version of numpy is 0x%x FATAL: module compiled as unknown endian FATAL: module compiled as little endian, but detected different endianness at runtime Class-level classmethod() can only be called on a method_descriptor or instance method. init sklearn.neighbors.dist_metrics sklearn.neighbors.dist_metrics.DiceDistance Dice Distance
Dice Distance is a dissimilarity measure for boolean-valued
vectors. All nonzero entries will be treated as True, zero entries will
be treated as False.
.. math::
D(x, y) = rac{N_{TF} + N_{FT}}{2 * N_{TT} + N_{TF} + N_{FT}}
sklearn.neighbors.dist_metrics.HaversineDistance Haversine (Spherical) Distance
The Haversine distance is the angular distance between two points on
the surface of a sphere. The first distance of each point is assumed
to be the latitude, the second is the longitude, given in radians.
The dimension of the points must be 2:
.. math::
D(x, y) = 2rcsin[\sqrt{\sin^2((x1 - y1) / 2)
+ cos(x1)cos(y1)sin^2((x2 - y2) / 2)}]
sklearn.neighbors.dist_metrics.ChebyshevDistance Chebyshev/Infinity Distance
.. math::
D(x, y) = max_i (|x_i - y_i|)
sklearn.neighbors.dist_metrics.MatchingDistance Matching Distance
Matching Distance is a dissimilarity measure for boolean-valued
vectors. All nonzero entries will be treated as True, zero entries will
be treated as False.
.. math::
D(x, y) = rac{N_{TF} + N_{FT}}{N}
sklearn.neighbors.dist_metrics.PyFuncDistance PyFunc Distance
A user-defined distance
Parameters
----------
func : function
func should take two numpy arrays as input, and return a distance.
sklearn.neighbors.dist_metrics.RogersTanimotoDistance Rogers-Tanimoto Distance
Rogers-Tanimoto Distance is a dissimilarity measure for boolean-valued
vectors. All nonzero entries will be treated as True, zero entries will
be treated as False.
.. math::
D(x, y) = rac{2 (N_{TF} + N_{FT})}{N + N_{TF} + N_{FT}}
sklearn.neighbors.dist_metrics._memoryviewslice Internal class for passing memoryview slices to Python sklearn.neighbors.dist_metrics.array sklearn.neighbors.dist_metrics.SokalMichenerDistance Sokal-Michener Distance
Sokal-Michener Distance is a dissimilarity measure for boolean-valued
vectors. All nonzero entries will be treated as True, zero entries will
be treated as False.
.. math::
D(x, y) = rac{2 (N_{TF} + N_{FT})}{N + N_{TF} + N_{FT}}
sklearn.neighbors.dist_metrics.ManhattanDistance Manhattan/City-block Distance metric
.. math::
D(x, y) = \sum_i |x_i - y_i|
sklearn.neighbors.dist_metrics.MahalanobisDistance Mahalanobis Distance
.. math::
D(x, y) = \sqrt{ (x - y)^T V^{-1} (x - y) }
Parameters
----------
V : array_like
Symmetric positive-definite covariance matrix.
The inverse of this matrix will be explicitly computed.
VI : array_like
optionally specify the inverse directly. If VI is passed,
then V is not referenced.
sklearn.neighbors.dist_metrics.MinkowskiDistance Minkowski Distance
.. math::
D(x, y) = [\sum_i (x_i - y_i)^p] ^ (1/p)
Minkowski Distance requires p >= 1 and finite. For p = infinity,
use ChebyshevDistance.
Note that for p=1, ManhattanDistance is more efficient, and for
p=2, EuclideanDistance is more efficient.
sklearn.neighbors.dist_metrics.WMinkowskiDistance Weighted Minkowski Distance
.. math::
D(x, y) = [\sum_i w_i (x_i - y_i)^p] ^ (1/p)
Weighted Minkowski Distance requires p >= 1 and finite.
Parameters
----------
p : int
The order of the norm of the difference :math:`{||u-v||}_p`.
w : (N,) array_like
The weight vector.
sklearn.neighbors.dist_metrics.EuclideanDistance Euclidean Distance metric
.. math::
D(x, y) = \sqrt{ \sum_i (x_i - y_i) ^ 2 }
sklearn.neighbors.dist_metrics.RussellRaoDistance Russell-Rao Distance
Russell-Rao Distance is a dissimilarity measure for boolean-valued
vectors. All nonzero entries will be treated as True, zero entries will
be treated as False.
.. math::
D(x, y) = rac{N - N_{TT}}{N}
sklearn.neighbors.dist_metrics.CanberraDistance Canberra Distance
Canberra distance is meant for discrete-valued vectors, though it is
a valid metric for real-valued vectors.
.. math::
D(x, y) = \sum_i rac{|x_i - y_i|}{|x_i| + |y_i|}
sklearn.neighbors.dist_metrics.BrayCurtisDistance Bray-Curtis Distance
Bray-Curtis distance is meant for discrete-valued vectors, though it is
a valid metric for real-valued vectors.
.. math::
D(x, y) = rac{\sum_i |x_i - y_i|}{\sum_i(|x_i| + |y_i|)}
sklearn.neighbors.dist_metrics.HammingDistance Hamming Distance
Hamming distance is meant for discrete-valued vectors, though it is
a valid metric for real-valued vectors.
.. math::
D(x, y) = rac{1}{N} \sum_i \delta_{x_i, y_i}
sklearn.neighbors.dist_metrics.JaccardDistance Jaccard Distance
Jaccard Distance is a dissimilarity measure for boolean-valued
vectors. All nonzero entries will be treated as True, zero entries will
be treated as False.
.. math::
D(x, y) = rac{N_{TF} + N_{FT}}{N_{TT} + N_{TF} + N_{FT}}
sklearn.neighbors.dist_metrics.SEuclideanDistance Standardized Euclidean Distance metric
.. math::
D(x, y) = \sqrt{ \sum_i rac{ (x_i - y_i) ^ 2}{V_i} }
sklearn.neighbors.dist_metrics.SokalSneathDistance Sokal-Sneath Distance
Sokal-Sneath Distance is a dissimilarity measure for boolean-valued
vectors. All nonzero entries will be treated as True, zero entries will
be treated as False.
.. math::
D(x, y) = rac{N_{TF} + N_{FT}}{N_{TT} / 2 + N_{TF} + N_{FT}}
sklearn.neighbors.dist_metrics.KulsinskiDistance Kulsinski Distance
Kulsinski Distance is a dissimilarity measure for boolean-valued
vectors. All nonzero entries will be treated as True, zero entries will
be treated as False.
.. math::
D(x, y) = 1 - rac{N_{TT}}{N + N_{TF} + N_{FT}}
sklearn.neighbors.dist_metrics.memoryview sklearn.neighbors.dist_metrics.DistanceMetric DistanceMetric class
This class provides a uniform interface to fast distance metric
functions. The various metrics can be accessed via the `get_metric`
class method and the metric string identifier (see below).
For example, to use the Euclidean distance:
>>> dist = DistanceMetric.get_metric('euclidean')
>>> X = [[0, 1, 2],
[3, 4, 5]])
>>> dist.pairwise(X)
array([[ 0. , 5.19615242],
[ 5.19615242, 0. ]])
Available Metrics
The following lists the string metric identifiers and the associated
distance metric classes:
**Metrics intended for real-valued vector spaces:**
============== ==================== ======== ===============================
identifier class name args distance function
-------------- -------------------- -------- -------------------------------
"euclidean" EuclideanDistance - ``sqrt(sum((x - y)^2))``
"manhattan" ManhattanDistance - ``sum(|x - y|)``
"chebyshev" ChebyshevDistance - ``sum(max(|x - y|))``
"minkowski" MinkowskiDistance p ``sum(|x - y|^p)^(1/p)``
"wminkowski" WMinkowskiDistance p, w ``sum(w * |x - y|^p)^(1/p)``
"seuclidean" SEuclideanDistance V ``sqrt(sum((x - y)^2 / V))``
"mahalanobis" MahalanobisDistance V or VI ``sqrt((x - y)' V^-1 (x - y))``
============== ==================== ======== ===============================
**Metrics intended for two-dimensional vector spaces:**
============ ================== ========================================
identifier class name distance function
------------ ------------------ ----------------------------------------
"haversine" HaversineDistance 2 arcsin(sqrt(sin^2(0.5*dx)
+ cos(x1)cos(x2)sin^2(0.5*dy)))
============ ================== ========================================
**Metrics intended for integer-valued vector spaces:** Though intended
for integer-valued vectors, these are also valid metrics in the case of
real-valued vectors.
============= ==================== ========================================
identifier class name distance function
------------- -------------------- ----------------------------------------
"hamming" HammingDistance ``N_unequal(x, y) / N_tot``
"canberra" CanberraDistance ``sum(|x - y| / (|x| + |y|))``
"braycurtis" BrayCurtisDistance ``sum(|x - y|) / (sum(|x|) + sum(|y|))``
============= ==================== ========================================
**Metrics intended for boolean-valued vector spaces:** Any nonzero entry
is evaluated to "True". In the listings below, the following
abbreviations are used:
- N : number of dimensions
- NTT : number of dims in which both values are True
- NTF : number of dims in which the first value is True, second is False
- NFT : number of dims in which the first value is False, second is True
- NFF : number of dims in which both values are False
- NNEQ : number of non-equal dimensions, NNEQ = NTF + NFT
- NNZ : number of nonzero dimensions, NNZ = NTF + NFT + NTT
================= ======================= ===============================
identifier class name distance function
----------------- ----------------------- -------------------------------
"jaccard" JaccardDistance NNEQ / NNZ
"maching" MatchingDistance NNEQ / N
"dice" DiceDistance NNEQ / (NTT + NNZ)
"kulsinski" KulsinskiDistance (NNEQ + N - NTT) / (NNEQ + N)
"rogerstanimoto" RogersTanimotoDistance 2 * NNEQ / (N + NNEQ)
"russellrao" RussellRaoDistance NNZ / N
"sokalmichener" SokalMichenerDistance 2 * NNEQ / (N + NNEQ)
"sokalsneath" SokalSneathDistance NNEQ / (NNEQ + 0.5 * NTT)
================= ======================= ===============================
**User-defined distance:**
=========== =============== =======
identifier class name args
----------- --------------- -------
"pyfunc" PyFuncDistance func
=========== =============== =======
Here ``func`` is a function which takes two one-dimensional numpy
arrays, and returns a distance. Note that in order to be used within
the BallTree, the distance must be a true metric:
i.e. it must satisfy the following properties
1) Non-negativity: d(x, y) >= 0
2) Identity: d(x, y) = 0 if and only if x == y
3) Symmetry: d(x, y) = d(y, x)
4) Triangle Inequality: d(x, y) + d(y, z) >= d(x, z)
Because of the Python object overhead involved in calling the python
function, this will be fairly slow, but it will have the same
scaling as other distances.
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# È
¨[ > þÿÿo ÿÿÿo ðÿÿo b ùÿÿo x ý" ¦f ¶f Æf Öf æf öf g g &g 6g Fg Vg fg vg g g ¦g ¶g Æg Ög æg ög h h &h 6h Fh Vh fh vh h h ¦h ¶h Æh Öh æh öh i i &i 6i Fi Vi fi vi i i ¦i ¶i Æi Öi æi öi j j &j 6j Fj Vj fj vj j j ¦j ¶j Æj Öj æj öj k k &k 6k Fk Vk fk vk k k ¦k ¶k Æk Ök æk ök l l &l 6l Fl Vl fl vl l l ¦l ¶l Æl Öl æl öl m m &m 6m Fm Vm fm vm m m ¦m ¶m Æm À# Compute the pairwise distances between X and Y
This is a convenience routine for the sake of testing. For many
metrics, the utilities in scipy.spatial.distance.cdist and
scipy.spatial.distance.pdist will be faster.
Parameters
----------
X : array_like
Array of shape (Nx, D), representing Nx points in D dimensions.
Y : array_like (optional)
Array of shape (Ny, D), representing Ny points in D dimensions.
If not specified, then Y=X.
Returns
-------
dist : ndarray
The shape (Nx, Ny) array of pairwise distances between points in
X and Y.
Convert the true distance to the reduced distance.
The reduced distance, defined for some metrics, is a computationally
more efficent measure which preserves the rank of the true distance.
For example, in the Euclidean distance metric, the reduced distance
is the squared-euclidean distance.
Convert the Reduced distance to the true distance.
The reduced distance, defined for some metrics, is a computationally
more efficent measure which preserves the rank of the true distance.
For example, in the Euclidean distance metric, the reduced distance
is the squared-euclidean distance.
Get the given distance metric from the string identifier.
See the docstring of DistanceMetric for a list of available metrics.
Parameters
----------
metric : string or class name
The distance metric to use
**kwargs
additional arguments will be passed to the requested metric
set state for pickling
get state for pickling
reduce method used for pickling
Given an iterable of metric class names or class identifiers,
return a list of metric IDs which map to those classes.
Example:
>>> L = get_valid_metric_ids([EuclideanDistance, 'ManhattanDistance'])
>>> sorted(L)
['cityblock', 'euclidean', 'l1', 'l2', 'manhattan']
__pyx_releasebuffer NotImplementedError dtype_is_object allocate_buffer __pyx_getbuffer rogerstanimoto __pyx_vtable__ METRIC_MAPPING sokalmichener __getstate__ RuntimeError sokalsneath mahalanobis MemoryError wminkowski seuclidean russellrao get_metric braycurtis __import__ ValueError IndexError minkowski manhattan kulsinski haversine euclidean enumerate cityblock chebyshev __class__ TypeError typedefs matching itemsize infinity canberra __test__ __name__ __main__ Ellipsis memview jaccard hamming fortran asarray __new__ xrange unpack struct random pyfunc newObj metric linalg format extend encode decode arcsin zeros start shape range order numpy items isinf flags error dtype ITYPE DTYPE ASCII stop step sqrt size pack ndim name mode join func dice base val sin pop obj key inv inf np l2 l1 id Zg Zf Zd VI <contiguous and indirect> <contiguous and direct> w q p l i h g f d c b Y X V Q O L I H C B <strided and indirect> <strided and direct> <strided and direct or indirect> getbuffer(obj, view, flags) Given an iterable of metric class names or class identifiers,
return a list of metric IDs which map to those classes.
Example:
>>> L = get_valid_metric_ids([EuclideanDistance, 'ManhattanDistance'])
>>> sorted(L)
['cityblock', 'euclidean', 'l1', 'l2', 'manhattan']
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