NDWasmAnalysis
- Description:
NDWasmAnalysis: Advanced Analysis, Stats, Spatial & Random Handles O(n log n) sorting, O(n*d^2) statistics, O(n^3) matrix properties, spatial clustering, and high-performance random sampling.
- Source:
Methods
(static) argsort(a) → {NDArray}
- Description:
Returns the indices that would sort an array.
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Parameters:
| Name | Type | Description |
|---|---|---|
a |
NDArray | Input array. |
Returns:
Indices as Int32 NDArray.
- Type
- NDArray
(static) cond(a, norm) → {number}
- Description:
estimates the reciprocal condition number of matrix a.
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Parameters:
| Name | Type | Description |
|---|---|---|
a |
NDArray | Input matrix. |
norm |
number | norm: 1 (1-norm) or Infinity (Infinity norm). |
Returns:
result.
- Type
- number
(static) corr(a) → {NDArray}
- Description:
Computes the Pearson correlation matrix for a dataset of shape [n_samples, n_features].
- Source:
Parameters:
| Name | Type | Description |
|---|---|---|
a |
NDArray | Data matrix. |
Returns:
Correlation matrix of shape [d, d].
- Type
- NDArray
(static) cov(a) → {NDArray}
- Description:
Computes the covariance matrix for a dataset of shape [n_samples, n_features].
- Source:
Parameters:
| Name | Type | Description |
|---|---|---|
a |
NDArray | Data matrix. |
Returns:
Covariance matrix of shape [d, d].
- Type
- NDArray
(static) eigenSym(a, computeVectors) → {Object}
- Description:
Eigenvalue decomposition for symmetric matrices.
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Parameters:
| Name | Type | Description |
|---|---|---|
a |
NDArray | Symmetric square matrix. |
computeVectors |
boolean | Whether to return eigenvectors. |
Returns:
- Type
- Object
(static) kmeans(data, k, maxIter) → {Object}
- Description:
Performs K-Means clustering in WASM memory.
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Parameters:
| Name | Type | Description |
|---|---|---|
data |
NDArray | Data of shape [n_samples, d_features]. |
k |
number | Number of clusters. |
maxIter |
number | Maximum iterations. |
Returns:
- Type
- Object
(static) kronecker()
- Description:
Computes the Kronecker product C = A ⊗ B.
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(static) norm(a, type) → {number}
- Description:
Computes the matrix norm.
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Parameters:
| Name | Type | Description |
|---|---|---|
a |
NDArray | Input matrix. |
type |
number | 1 (The maximum absolute column sum), 2 (Frobenius), Infinity (The maximum absolute row sum) |
Returns:
The norm value.
- Type
- number
(static) pairwiseDist(a, b) → {NDArray}
- Description:
Computes pairwise Euclidean distances between two sets of vectors.
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Parameters:
| Name | Type | Description |
|---|---|---|
a |
NDArray | Matrix of shape [m, d]. |
b |
NDArray | Matrix of shape [n, d]. |
Returns:
Distance matrix of shape [m, n].
- Type
- NDArray
(static) rank(a, tol) → {number}
- Description:
Computes the rank of a matrix using SVD.
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Parameters:
| Name | Type | Description |
|---|---|---|
a |
NDArray | Input matrix. |
tol |
number | Tolerance for singular values (0 for 1e-14). |
Returns:
Integer rank of the matrix.
- Type
- number
(static) topk(a, k, largest) → {Object}
- Description:
Finds the largest or smallest K elements and their indices. Complexity: O(n log k)
- Source:
Parameters:
| Name | Type | Description |
|---|---|---|
a |
NDArray | Input array. |
k |
number | Number of elements to return. |
largest |
boolean | If true, find max elements; else min. |
Returns:
- Type
- Object