{ "__type": "IngestedDoc", "__tag": 4010, "_content": { "Notes": { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "The following are common calling conventions:" } ] }, { "__type": "BulletList", "__tag": 4053, "ordered": true, "start": 1, "children": [ { "__type": "ListItem", "__tag": 4054, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "InlineCode", "__tag": 4051, "value": "Y = cdist(XA, XB, 'euclidean')" } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Computes the distance between " }, { "__type": "InlineMath", "__tag": 4057, "value": "m" }, { "__type": "Text", "__tag": 4046, "value": " points using Euclidean distance (2-norm) as the distance metric between the points. The points are arranged as " }, { "__type": "InlineMath", "__tag": 4057, "value": "m" }, { "__type": "Text", "__tag": 4046, "value": " " }, { "__type": "InlineMath", "__tag": 4057, "value": "n" }, { "__type": "Text", "__tag": 4046, "value": "-dimensional row vectors in the matrix X." } ] } ] }, { "__type": "ListItem", "__tag": 4054, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "InlineCode", "__tag": 4051, "value": "Y = cdist(XA, XB, 'minkowski', p=2.)" } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Computes the distances using the Minkowski distance " }, { "__type": "InlineMath", "__tag": 4057, "value": "\\|u-v\\|_p" }, { "__type": "Text", "__tag": 4046, "value": " (" }, { "__type": "InlineMath", "__tag": 4057, "value": "p" }, { "__type": "Text", "__tag": 4046, "value": "-norm) where " }, { "__type": "InlineMath", "__tag": 4057, "value": "p > 0" }, { "__type": "Text", "__tag": 4046, "value": " (note that this is only a quasi-metric if " }, { "__type": "InlineMath", "__tag": 4057, "value": "0 < p < 1" }, { "__type": "Text", "__tag": 4046, "value": ")." } ] } ] }, { "__type": "ListItem", "__tag": 4054, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "InlineCode", "__tag": 4051, "value": "Y = cdist(XA, XB, 'cityblock')" } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Computes the city block or Manhattan distance between the points." } ] } ] }, { "__type": "ListItem", "__tag": 4054, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "InlineCode", "__tag": 4051, "value": "Y = cdist(XA, XB, 'seuclidean', V=None)" } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Computes the standardized Euclidean distance. The standardized Euclidean distance between two n-vectors " }, { "__type": "InlineCode", "__tag": 4051, "value": "u" }, { "__type": "Text", "__tag": 4046, "value": " and " }, { "__type": "InlineCode", "__tag": 4051, "value": "v" }, { "__type": "Text", "__tag": 4046, "value": " is" } ] }, { "__type": "Math", "__tag": 4058, "value": "\\sqrt{\\sum {(u_i-v_i)^2 / V[x_i]}}." }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "V is the variance vector; V[i] is the variance computed over all the i'th components of the points. If not passed, it is automatically computed." } ] } ] }, { "__type": "ListItem", "__tag": 4054, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "InlineCode", "__tag": 4051, "value": "Y = cdist(XA, XB, 'sqeuclidean')" } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Computes the squared Euclidean distance " }, { "__type": "InlineMath", "__tag": 4057, "value": "\\|u-v\\|_2^2" }, { "__type": "Text", "__tag": 4046, "value": " between the vectors." } ] } ] }, { "__type": "ListItem", "__tag": 4054, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "InlineCode", "__tag": 4051, "value": "Y = cdist(XA, XB, 'cosine')" } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Computes the cosine distance between vectors u and v," } ] }, { "__type": "Math", "__tag": 4058, "value": "1 - \\frac{u \\cdot v}\n {{\\|u\\|}_2 {\\|v\\|}_2}" }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "where " }, { "__type": "InlineMath", "__tag": 4057, "value": "\\|*\\|_2" }, { "__type": "Text", "__tag": 4046, "value": " is the 2-norm of its argument " }, { "__type": "InlineCode", "__tag": 4051, "value": "*" }, { "__type": "Text", "__tag": 4046, "value": ", and " }, { "__type": "InlineMath", "__tag": 4057, "value": "u \\cdot v" }, { "__type": "Text", "__tag": 4046, "value": " is the dot product of " }, { "__type": "InlineMath", "__tag": 4057, "value": "u" }, { "__type": "Text", "__tag": 4046, "value": " and " }, { "__type": "InlineMath", "__tag": 4057, "value": "v" }, { "__type": "Text", "__tag": 4046, "value": "." } ] } ] }, { "__type": "ListItem", "__tag": 4054, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "InlineCode", "__tag": 4051, "value": "Y = cdist(XA, XB, 'correlation')" } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Computes the correlation distance between vectors u and v. This is" } ] }, { "__type": "Math", "__tag": 4058, "value": "1 - \\frac{(u - \\bar{u}) \\cdot (v - \\bar{v})}\n {{\\|(u - \\bar{u})\\|}_2 {\\|(v - \\bar{v})\\|}_2}" }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "where " }, { "__type": "InlineMath", "__tag": 4057, "value": "\\bar{v}" }, { "__type": "Text", "__tag": 4046, "value": " is the mean of the elements of vector v, and " }, { "__type": "InlineMath", "__tag": 4057, "value": "x \\cdot y" }, { "__type": "Text", "__tag": 4046, "value": " is the dot product of " }, { "__type": "InlineMath", "__tag": 4057, "value": "x" }, { "__type": "Text", "__tag": 4046, "value": " and " }, { "__type": "InlineMath", "__tag": 4057, "value": "y" }, { "__type": "Text", "__tag": 4046, "value": "." } ] } ] }, { "__type": "ListItem", "__tag": 4054, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "InlineCode", "__tag": 4051, "value": "Y = cdist(XA, XB, 'hamming')" } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Computes the normalized Hamming distance, or the proportion of those vector elements between two n-vectors " }, { "__type": "InlineCode", "__tag": 4051, "value": "u" }, { "__type": "Text", "__tag": 4046, "value": " and " }, { "__type": "InlineCode", "__tag": 4051, "value": "v" }, { "__type": "Text", "__tag": 4046, "value": " which disagree. To save memory, the matrix " }, { "__type": "InlineCode", "__tag": 4051, "value": "X" }, { "__type": "Text", "__tag": 4046, "value": " can be of type boolean." } ] } ] }, { "__type": "ListItem", "__tag": 4054, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "InlineCode", "__tag": 4051, "value": "Y = cdist(XA, XB, 'jaccard')" } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Computes the Jaccard distance between the points. Given two vectors, " }, { "__type": "InlineCode", "__tag": 4051, "value": "u" }, { "__type": "Text", "__tag": 4046, "value": " and " }, { "__type": "InlineCode", "__tag": 4051, "value": "v" }, { "__type": "Text", "__tag": 4046, "value": ", the Jaccard distance is the proportion of those elements " }, { "__type": "InlineCode", "__tag": 4051, "value": "u[i]" }, { "__type": "Text", "__tag": 4046, "value": " and " }, { "__type": "InlineCode", "__tag": 4051, "value": "v[i]" }, { "__type": "Text", "__tag": 4046, "value": " that disagree where at least one of them is non-zero." } ] } ] }, { "__type": "ListItem", "__tag": 4054, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "InlineCode", "__tag": 4051, "value": "Y = cdist(XA, XB, 'jensenshannon')" } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Computes the Jensen-Shannon distance between two probability arrays. Given two probability vectors, " }, { "__type": "InlineMath", "__tag": 4057, "value": "p" }, { "__type": "Text", "__tag": 4046, "value": " and " }, { "__type": "InlineMath", "__tag": 4057, "value": "q" }, { "__type": "Text", "__tag": 4046, "value": ", the Jensen-Shannon distance is" } ] }, { "__type": "Math", "__tag": 4058, "value": "\\sqrt{\\frac{D(p \\parallel m) + D(q \\parallel m)}{2}}" }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "where " }, { "__type": "InlineMath", "__tag": 4057, "value": "m" }, { "__type": "Text", "__tag": 4046, "value": " is the pointwise mean of " }, { "__type": "InlineMath", "__tag": 4057, "value": "p" }, { "__type": "Text", "__tag": 4046, "value": " and " }, { "__type": "InlineMath", "__tag": 4057, "value": "q" }, { "__type": "Text", "__tag": 4046, "value": " and " }, { "__type": "InlineMath", "__tag": 4057, "value": "D" }, { "__type": "Text", "__tag": 4046, "value": " is the Kullback-Leibler divergence." } ] } ] }, { "__type": "ListItem", "__tag": 4054, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "InlineCode", "__tag": 4051, "value": "Y = cdist(XA, XB, 'chebyshev')" } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Computes the Chebyshev distance between the points. The Chebyshev distance between two n-vectors " }, { "__type": "InlineCode", "__tag": 4051, "value": "u" }, { "__type": "Text", "__tag": 4046, "value": " and " }, { "__type": "InlineCode", "__tag": 4051, "value": "v" }, { "__type": "Text", "__tag": 4046, "value": " is the maximum norm-1 distance between their respective elements. More precisely, the distance is given by" } ] }, { "__type": "Math", "__tag": 4058, "value": "d(u,v) = \\max_i {|u_i-v_i|}." } ] }, { "__type": "ListItem", "__tag": 4054, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "InlineCode", "__tag": 4051, "value": "Y = cdist(XA, XB, 'canberra')" } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Computes the Canberra distance between the points. The Canberra distance between two points " }, { "__type": "InlineCode", "__tag": 4051, "value": "u" }, { "__type": "Text", "__tag": 4046, "value": " and " }, { "__type": "InlineCode", "__tag": 4051, "value": "v" }, { "__type": "Text", "__tag": 4046, "value": " is" } ] }, { "__type": "Math", "__tag": 4058, "value": "d(u,v) = \\sum_i \\frac{|u_i-v_i|}\n {|u_i|+|v_i|}." } ] }, { "__type": "ListItem", "__tag": 4054, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "InlineCode", "__tag": 4051, "value": "Y = cdist(XA, XB, 'braycurtis')" } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Computes the Bray-Curtis distance between the points. The Bray-Curtis distance between two points " }, { "__type": "InlineCode", "__tag": 4051, "value": "u" }, { "__type": "Text", "__tag": 4046, "value": " and " }, { "__type": "InlineCode", "__tag": 4051, "value": "v" }, { "__type": "Text", "__tag": 4046, "value": " is" } ] }, { "__type": "Math", "__tag": 4058, "value": "d(u,v) = \\frac{\\sum_i (|u_i-v_i|)}\n {\\sum_i (|u_i+v_i|)}" } ] }, { "__type": "ListItem", "__tag": 4054, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "InlineCode", "__tag": 4051, "value": "Y = cdist(XA, XB, 'mahalanobis', VI=None)" } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Computes the Mahalanobis distance between the points. The Mahalanobis distance between two points " }, { "__type": "InlineCode", "__tag": 4051, "value": "u" }, { "__type": "Text", "__tag": 4046, "value": " and " }, { "__type": "InlineCode", "__tag": 4051, "value": "v" }, { "__type": "Text", "__tag": 4046, "value": " is " }, { "__type": "InlineMath", "__tag": 4057, "value": "\\sqrt{(u-v)(1/V)(u-v)^T}" }, { "__type": "Text", "__tag": 4046, "value": " where " }, { "__type": "InlineMath", "__tag": 4057, "value": "(1/V)" }, { "__type": "Text", "__tag": 4046, "value": " (the " }, { "__type": "InlineCode", "__tag": 4051, "value": "VI" }, { "__type": "Text", "__tag": 4046, "value": " variable) is the inverse covariance. If " }, { "__type": "InlineCode", "__tag": 4051, "value": "VI" }, { "__type": "Text", "__tag": 4046, "value": " is not None, " }, { "__type": "InlineCode", "__tag": 4051, "value": "VI" }, { "__type": "Text", "__tag": 4046, "value": " will be used as the inverse covariance matrix." } ] } ] }, { "__type": "ListItem", "__tag": 4054, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "InlineCode", "__tag": 4051, "value": "Y = cdist(XA, XB, 'yule')" } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Computes the Yule distance between the boolean vectors. (see " }, { "__type": "CrossRef", "__tag": 4002, "value": "yule", "reference": { "__type": "LocalRef", "__tag": 4022, "kind": "module", "path": "scipy.spatial.distance:yule" }, "kind": "module" }, { "__type": "Text", "__tag": 4046, "value": " function documentation)" } ] } ] }, { "__type": "ListItem", "__tag": 4054, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "InlineCode", "__tag": 4051, "value": "Y = cdist(XA, XB, 'matching')" } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Synonym for 'hamming'." } ] } ] }, { "__type": "ListItem", "__tag": 4054, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "InlineCode", "__tag": 4051, "value": "Y = cdist(XA, XB, 'dice')" } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Computes the Dice distance between the boolean vectors. (see " }, { "__type": "CrossRef", "__tag": 4002, "value": "dice", "reference": { "__type": "LocalRef", "__tag": 4022, "kind": "module", "path": "scipy.spatial.distance:dice" }, "kind": "module" }, { "__type": "Text", "__tag": 4046, "value": " function documentation)." } ] } ] }, { "__type": "ListItem", "__tag": 4054, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "InlineCode", "__tag": 4051, "value": "Y = cdist(XA, XB, 'rogerstanimoto')" } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Computes the Rogers-Tanimoto distance between the boolean vectors. (see " }, { "__type": "CrossRef", "__tag": 4002, "value": "rogerstanimoto", "reference": { "__type": "LocalRef", "__tag": 4022, "kind": "module", "path": "scipy.spatial.distance:rogerstanimoto" }, "kind": "module" }, { "__type": "Text", "__tag": 4046, "value": " function documentation)" } ] } ] }, { "__type": "ListItem", "__tag": 4054, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "InlineCode", "__tag": 4051, "value": "Y = cdist(XA, XB, 'russellrao')" } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Computes the Russell-Rao distance between the boolean vectors. (see " }, { "__type": "CrossRef", "__tag": 4002, "value": "russellrao", "reference": { "__type": "LocalRef", "__tag": 4022, "kind": "module", "path": "scipy.spatial.distance:russellrao" }, "kind": "module" }, { "__type": "Text", "__tag": 4046, "value": " function documentation)" } ] } ] }, { "__type": "ListItem", "__tag": 4054, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "InlineCode", "__tag": 4051, "value": "Y = cdist(XA, XB, 'sokalsneath')" } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Computes the Sokal-Sneath distance between the vectors. (see " }, { "__type": "CrossRef", "__tag": 4002, "value": "sokalsneath", "reference": { "__type": "LocalRef", "__tag": 4022, "kind": "module", "path": "scipy.spatial.distance:sokalsneath" }, "kind": "module" }, { "__type": "Text", "__tag": 4046, "value": " function documentation)" } ] } ] }, { "__type": "ListItem", "__tag": 4054, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "InlineCode", "__tag": 4051, "value": "Y = cdist(XA, XB, f)" } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Computes the distance between all pairs of vectors in X using the user supplied 2-arity function f. For example, Euclidean distance between the vectors could be computed as follows " } ] }, { "__type": "Code", "__tag": 4050, "value": "dm = cdist(XA, XB, lambda u, v: np.sqrt(((u-v)**2).sum()))", "execution_status": null }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Note that you should avoid passing a reference to one of the distance functions defined in this library. For example, " } ] }, { "__type": "Code", "__tag": 4050, "value": "dm = cdist(XA, XB, sokalsneath)", "execution_status": null }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "would calculate the pair-wise distances between the vectors in X using the Python function " }, { "__type": "CrossRef", "__tag": 4002, "value": "sokalsneath", "reference": { "__type": "LocalRef", "__tag": 4022, "kind": "module", "path": "scipy.spatial.distance:sokalsneath" }, "kind": "module" }, { "__type": "Text", "__tag": 4046, "value": ". This would result in sokalsneath being called " }, { "__type": "InlineMath", "__tag": 4057, "value": "{n \\choose 2}" }, { "__type": "Text", "__tag": 4046, "value": " times, which is inefficient. Instead, the optimized C version is more efficient, and we call it using the following syntax " } ] }, { "__type": "Code", "__tag": 4050, "value": "dm = cdist(XA, XB, 'sokalsneath')", "execution_status": null } ] } ] } ], "title": [], "level": 0, "target": null }, "Warns": { "__type": "Section", "__tag": 4015, "children": [], "title": [], "level": 0, "target": null }, "Raises": { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Parameters", "__tag": 4026, "children": [ { "__type": "DocParam", "__tag": 4016, "name": "", "annotation": "ValueError", "desc": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "An exception is thrown if " }, { "__type": "ParamRef", "__tag": 4071, "name": "XA" }, { "__type": "Text", "__tag": 4046, "value": " and " }, { "__type": "ParamRef", "__tag": 4071, "name": "XB" }, { "__type": "Text", "__tag": 4046, "value": " do not have the same number of columns." } ] } ] } ] } ], "title": [], "level": 0, "target": null }, "Yields": { "__type": "Section", "__tag": 4015, "children": [], "title": [], "level": 0, "target": null }, "Methods": { "__type": "Section", "__tag": 4015, "children": [], "title": [], "level": 0, "target": null }, "Returns": { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Parameters", "__tag": 4026, "children": [ { "__type": "DocParam", "__tag": 4016, "name": "Y", "annotation": "ndarray", "desc": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "A " }, { "__type": "InlineMath", "__tag": 4057, "value": "m_A" }, { "__type": "Text", "__tag": 4046, "value": " by " }, { "__type": "InlineMath", "__tag": 4057, "value": "m_B" }, { "__type": "Text", "__tag": 4046, "value": " distance matrix is returned. For each " }, { "__type": "InlineMath", "__tag": 4057, "value": "i" }, { "__type": "Text", "__tag": 4046, "value": " and " }, { "__type": "InlineMath", "__tag": 4057, "value": "j" }, { "__type": "Text", "__tag": 4046, "value": ", the metric " }, { "__type": "InlineCode", "__tag": 4051, "value": "dist(u=XA[i], v=XB[j])" }, { "__type": "Text", "__tag": 4046, "value": " is computed and stored in the " }, { "__type": "InlineMath", "__tag": 4057, "value": "ij" }, { "__type": "Text", "__tag": 4046, "value": " th entry." } ] } ] } ] } ], "title": [], "level": 0, "target": null }, "Summary": { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Compute distance between each pair of the two collections of inputs." } ] } ], "title": [], "level": 0, "target": null }, "Receives": { "__type": "Section", "__tag": 4015, "children": [], "title": [], "level": 0, "target": null }, "Warnings": { "__type": "Section", "__tag": 4015, "children": [], "title": [], "level": 0, "target": null }, "Attributes": { "__type": "Section", "__tag": 4015, "children": [], "title": [], "level": 0, "target": null }, "Parameters": { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Parameters", "__tag": 4026, "children": [ { "__type": "DocParam", "__tag": 4016, "name": "XA", "annotation": "array_like", "desc": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "An " }, { "__type": "InlineMath", "__tag": 4057, "value": "m_A" }, { "__type": "Text", "__tag": 4046, "value": " by " }, { "__type": "InlineMath", "__tag": 4057, "value": "n" }, { "__type": "Text", "__tag": 4046, "value": " array of " }, { "__type": "InlineMath", "__tag": 4057, "value": "m_A" }, { "__type": "Text", "__tag": 4046, "value": " original observations in an " }, { "__type": "InlineMath", "__tag": 4057, "value": "n" }, { "__type": "Text", "__tag": 4046, "value": "-dimensional space. Inputs are converted to float type." } ] } ] }, { "__type": "DocParam", "__tag": 4016, "name": "XB", "annotation": "array_like", "desc": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "An " }, { "__type": "InlineMath", "__tag": 4057, "value": "m_B" }, { "__type": "Text", "__tag": 4046, "value": " by " }, { "__type": "InlineMath", "__tag": 4057, "value": "n" }, { "__type": "Text", "__tag": 4046, "value": " array of " }, { "__type": "InlineMath", "__tag": 4057, "value": "m_B" }, { "__type": "Text", "__tag": 4046, "value": " original observations in an " }, { "__type": "InlineMath", "__tag": 4057, "value": "n" }, { "__type": "Text", "__tag": 4046, "value": "-dimensional space. Inputs are converted to float type." } ] } ] }, { "__type": "DocParam", "__tag": 4016, "name": "metric", "annotation": "str or callable, optional", "desc": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "The distance metric to use. If a string, the distance function can be 'braycurtis', 'canberra', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'euclidean', 'hamming', 'jaccard', 'jensenshannon', 'mahalanobis', 'matching', 'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalsneath', 'sqeuclidean', 'yule'." } ] } ] }, { "__type": "DocParam", "__tag": 4016, "name": "**kwargs", "annotation": "dict, optional", "desc": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Extra arguments to " }, { "__type": "ParamRef", "__tag": 4071, "name": "metric" }, { "__type": "Text", "__tag": 4046, "value": ": refer to each metric documentation for a list of all possible arguments." } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Some possible arguments:" } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "pscalar The p-norm to apply for Minkowski, weighted and unweighted. Default: 2." } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "warray_like The weight vector for metrics that support weights (e.g., Minkowski)." } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Varray_like The variance vector for standardized Euclidean. Default: var(vstack([XA, XB]), axis=0, ddof=1)" } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "VIarray_like The inverse of the covariance matrix for Mahalanobis. Default: inv(cov(vstack([XA, XB].T))).T" } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "outndarray The output array If not None, the distance matrix Y is stored in this array." } ] } ] } ] } ], "title": [], "level": 0, "target": null }, "Extended Summary": { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "See Notes for common calling conventions." } ] } ], "title": [], "level": 0, "target": null }, "Other Parameters": { "__type": "Section", "__tag": 4015, "children": [], "title": [], "level": 0, "target": null } }, "_ordered_sections": [ "Summary", "Extended Summary", "Parameters", "Attributes", "Methods", "Returns", "Yields", "Receives", "Other Parameters", "Raises", "Warns", "Warnings", "Notes" ], "item_file": "/scipy/spatial/distance.py", "item_line": 2606, "item_type": "function", "aliases": [ "scipy.cluster.vq.cdist" ], "example_section_data": { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Text", "__tag": 4046, "value": "Find the Euclidean distances between four 2-D coordinates:\n\n" }, { "__type": "Code", "__tag": 4050, "value": "from scipy.spatial import distance\nimport numpy as np\ncoords = [(35.0456, -85.2672),\n (35.1174, -89.9711),\n (35.9728, -83.9422),\n (36.1667, -86.7833)]\n", "execution_status": "success" }, { "__type": "Code", "__tag": 4050, "value": "distance.cdist(coords, coords, 'euclidean')\n", "execution_status": "failure" }, { "__type": "Text", "__tag": 4046, "value": "\nFind the Manhattan distance from a 3-D point to the corners of the unit\ncube:\n\n" }, { "__type": "Code", "__tag": 4050, "value": "a = np.array([[0, 0, 0],\n [0, 0, 1],\n [0, 1, 0],\n [0, 1, 1],\n [1, 0, 0],\n [1, 0, 1],\n [1, 1, 0],\n [1, 1, 1]])\nb = np.array([[ 0.1, 0.2, 0.4]])\n", "execution_status": "success" }, { "__type": "Code", "__tag": 4050, "value": "distance.cdist(a, b, 'cityblock')\n", "execution_status": "failure" } ], "title": [], "level": 0, "target": null }, "see_also": [], "signature": { "__type": "SignatureNode", "__tag": 4029, "kind": "function", "parameters": [ { "__type": "SigParam", "__tag": 4030, "name": "XA", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": { "__type": "Empty", "__tag": 4031 } }, { "__type": "SigParam", "__tag": 4030, "name": "XB", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": { "__type": "Empty", "__tag": 4031 } }, { "__type": "SigParam", "__tag": 4030, "name": "metric", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": "euclidean" }, { "__type": "SigParam", "__tag": 4030, "name": "out", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "KEYWORD_ONLY", "default": "None" }, { "__type": "SigParam", "__tag": 4030, "name": "kwargs", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "VAR_KEYWORD", "default": { "__type": "Empty", "__tag": 4031 } } ], "return_annotation": { "__type": "Empty", "__tag": 4031 }, "target_name": "cdist" }, "references": null, "qa": "scipy.spatial.distance:cdist", "arbitrary": [], "local_refs": [ "**kwargs", "XA", "XB", "Y", "metric" ] }