spateo.tdr.widgets.tree#
Module Contents#
Classes#
This is a global solver for principal curves that uses neural networks. |
Functions#
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solve the problem size(C) = NxN, size(W) = NxL. max_W trace( W' C W ) : W' W = I. |
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This function returns an array containing m (n) copies of A in the row (column) dimensions. The size of B is |
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calculate the square distance between a, b. |
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This function is a pure Python implementation of the DDRTree algorithm. |
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Loss function for the NLPCA NN. Returns the sum of the orthogonal |
- spateo.tdr.widgets.tree.pca_projection(C: numpy.ndarray, L: int)[source]#
solve the problem size(C) = NxN, size(W) = NxL. max_W trace( W’ C W ) : W’ W = I.
- Parameters
- C
An array like matrix.
- L
The number of Eigenvalues.
- Returns
The L largest Eigenvalues.
- Return type
W
- spateo.tdr.widgets.tree.repmat(X: numpy.ndarray, m: int, n: int) numpy.ndarray[source]#
This function returns an array containing m (n) copies of A in the row (column) dimensions. The size of B is size(A)*n when A is a matrix.For example, repmat(np.matrix(1:4), 2, 3) returns a 4-by-6 matrix.
- Parameters
- X
An array like matrix.
- m
Number of copies on row dimension
- n
Number of copies on column dimension
- Returns
A matrix of repmat.
- Return type
xy_rep
- spateo.tdr.widgets.tree.sqdist(a: numpy.ndarray, b: numpy.ndarray) numpy.ndarray[source]#
calculate the square distance between a, b.
- Parameters
- a
A matrix with \(D imes N\) dimension
- b
A matrix with \(D imes N\) dimension
- Returns
A numeric value for the different between a and b
- Return type
dist
- spateo.tdr.widgets.tree.DDRTree(X: numpy.ndarray, maxIter: int = 20, sigma: Union[int, float] = 0.001, gamma: Union[int, float] = 10, eps: int = 0, dim: int = 2, Lambda: Union[int, float] = 1.0, ncenter: Optional[int] = None)[source]#
This function is a pure Python implementation of the DDRTree algorithm.
- Parameters
- X
DxN, data matrix list.
- maxIter
Maximum iterations.
- eps
Relative objective difference.
- dim
Reduced dimension.
- Lambda
Regularization parameter for inverse graph embedding.
- sigma
Bandwidth parameter.
- gamma
Regularization parameter for k-means.
- ncenter
number of nodes allowed in the regularization graph
- Returns
- A tuple of Z, Y, stree, R, W, Q, C, objs
W is the orthogonal set of d (dimensions) linear basis vector Z is the reduced dimension space stree is the smooth tree graph embedded in the low dimension space Y represents latent points as the center of
- spateo.tdr.widgets.tree.orth_dist(y_true, y_pred)[source]#
Loss function for the NLPCA NN. Returns the sum of the orthogonal distance from the output tensor to the real tensor.
- class spateo.tdr.widgets.tree.NLPCA[source]#
Bases:
objectThis is a global solver for principal curves that uses neural networks. .. attribute:: None
- fit(data: numpy.ndarray, epochs: int = 500, nodes: int = 25, lr: float = 0.01, verbose: int = 0)[source]#
This method creates a model and will fit it to the given m x n dimensional data.
- Parameters
- data
A numpy array of shape (m,n), where m is the number of points and n is the number of dimensions.
- epochs
Number of epochs to train neural network, defaults to 500.
- nodes
Number of nodes for the construction layers. Defaults to 25. The more complex the curve, the higher this number should be.
- lr
Learning rate for backprop. Defaults to .01
- verbose
Verbose = 0 mutes the training text from Keras. Defaults to 0.
- project(data: numpy.ndarray) Tuple[numpy.ndarray, numpy.ndarray][source]#
The project function will project the points to the curve generated by the fit function. Given back is the projection index of the original data and a sorted version of the original data.
- Parameters
- data
m x n array to project to the curve
- Returns
A one-dimension array that contains the projection index for each point in data. all_sorted: A m x n+1 array that contains data sorted by its projection index, along with the index.
- Return type
proj