copulas.multivariate.tree module¶
Multivariate trees module.
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class
copulas.multivariate.tree.
CenterTree
(random_state=None)[source]¶ Bases:
copulas.multivariate.tree.Tree
Tree for a C-vine copula.
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get_anchor
()[source]¶ Find anchor variable with highest sum of dependence with the rest.
- Returns
Anchor variable.
- Return type
int
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tree_type
= 0¶
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class
copulas.multivariate.tree.
DirectTree
(random_state=None)[source]¶ Bases:
copulas.multivariate.tree.Tree
DirectTree class.
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tree_type
= 1¶
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class
copulas.multivariate.tree.
Edge
(index, left, right, copula_name, copula_theta)[source]¶ Bases:
object
Represents an edge in the copula.
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classmethod
from_dict
(edge_dict)[source]¶ Create a new instance from a parameters dictionary.
- Parameters
params (dict) – Parameters of the Edge, in the same format as the one returned by the
to_dict
method.- Returns
Instance of the edge defined on the parameters.
- Return type
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classmethod
get_child_edge
(index, left_parent, right_parent)[source]¶ Construct a child edge from two parent edges.
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classmethod
get_conditional_uni
(left_parent, right_parent)[source]¶ Identify pair univariate value from parents.
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get_likelihood
(uni_matrix)[source]¶ Compute likelihood given a U matrix.
- Parameters
uni_matrix (numpy.array) – Matrix to compute the likelihood.
- Returns
likelihood and conditional values.
- Return type
tuple (np.ndarray, np.ndarray, np.array)
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is_adjacent
(another_edge)[source]¶ Check if two edges are adjacent.
- Parameters
another_edge (Edge) – edge object of another edge
- Returns
True if the two edges are adjacent.
- Return type
bool
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classmethod
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class
copulas.multivariate.tree.
RegularTree
(random_state=None)[source]¶ Bases:
copulas.multivariate.tree.Tree
RegularTree class.
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tree_type
= 2¶
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class
copulas.multivariate.tree.
Tree
(random_state=None)[source]¶ Bases:
copulas.multivariate.base.Multivariate
Helper class to instantiate a single tree in the vine model.
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fit
(index, n_nodes, tau_matrix, previous_tree, edges=None)[source]¶ Fit this tree object.
- Parameters
index (int) – index of the tree.
n_nodes (int) – number of nodes in the tree.
tau_matrix (numpy.array) – kendall’s tau matrix of the data, shape (n_nodes, n_nodes).
previous_tree (Tree) – tree object of previous level.
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fitted
= False¶
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classmethod
from_dict
(tree_dict, previous=None)[source]¶ Create a new instance from a parameters dictionary.
- Parameters
params (dict) – Parameters of the Tree, in the same format as the one returned by the
to_dict
method.- Returns
Instance of the tree defined on the parameters.
- Return type
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get_adjacent_matrix
()[source]¶ Get adjacency matrix.
- Returns
adjacency matrix
- Return type
numpy.ndarray
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get_likelihood
(uni_matrix)[source]¶ Compute likelihood of the tree given an U matrix.
- Parameters
uni_matrix (numpy.array) – univariate matrix to evaluate likelihood on.
- Returns
likelihood of the current tree, next level conditional univariate matrix
- Return type
tuple[float, numpy.array]
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get_tau_matrix
()[source]¶ Get tau matrix for adjacent pairs.
- Returns
tau matrix for the current tree
- Return type
tau (numpy.ndarray)
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to_dict
()[source]¶ Return a dict with the parameters to replicate this Tree.
- Returns
Parameters of this Tree.
- Return type
dict
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tree_type
= None¶
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