Cheatography
https://cheatography.com
Variety of functions and useful things with a special focus on linear algebra/matricial stuff.
This is a draft cheat sheet. It is a work in progress and is not finished yet.
Libraries
Numpy |
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Linear algebra (numpy) |
import numpy.linalg as LA
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Sparse operations (scipy) |
import scipy.sparse as ss
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Sparse normalizations (sklearn) |
from sklearn.preprocessing import normalize as sparse_norm
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Array comparisons
Locate indices based on condition |
index = np.where(array == 4)
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Check if all/any satisfy condition |
np.any(array == 1), np.all(array1 == array2)
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Matricial operations
Dot product |
np.dot(v1, v2)
or v1 @ v2
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Outer product |
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Sum rows (0), columns (1) |
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Eigenvector, eigenvalues |
eigval, eigvec = LA.eig(A)
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The eigenvector output of the eig()
function is a matrix containing the usual eigenvectors as its columns.
Numpy comparisons
Check if any element satisfies a condition |
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Find which elements (their indices) satisfy a condition |
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Check if all elements satisfies a condition |
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Closeness up to tolerance |
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Numpy utilities
Arrange all array elements in a list |
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Sparse matrices with scipy
Convert numpy matrices to sparse |
A_sparse = ss.csr_matrix(A)
A_sparse = ss.csc_matrix(A)
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Change CSC to CSR and viceversa |
A_csr = A_csc.tocsr()
A_csc = A_csr.tocsc()
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Row/col. normalization |
A_norm = sparse_normalize(A_csc, norm='l1', axis=1)
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Sparse identity matrix |
sparse_id = ss.identity(n)
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Invert a (CSC) sparse matrix |
sparse_inv = ss.inv(A_sparse)
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Dot product with vector |
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Transpose sparse matrix |
At_sparse = A_sparse.transpose()
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CSC (Compressed Sparse Column) are more suitable for column operations while CSR (Compressed Sparse Row) are for row operations. For instance, row/col. normalizations.
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