pysparse.cs.sensing package

Submodules

pysparse.cs.sensing.obsmat module

pysparse.cs.sensing.obsmat.bernoulli(shape, seed=None, verbose=True)

return a matrix, which have bernoulli distribution elements columns are l2 normalized

pysparse.cs.sensing.obsmat.bernoulli0(shape, seed=None, verbose=True)

generates Bernoulli observation matrix

Generates M-by-N Bernoulli observation matrix

${\bm \Phi}_{ij} =\left\{\begin{array}{cc}{+\frac{1}{\sqrt{M}}} & {P=\frac{1}{2}} \\ {-\frac{1}{\sqrt{M}}} & {P=\frac{1}{2}}\end{array}= \frac{1}{\sqrt{M}}\left\{\begin{array}{cc}{+1} & {P=\frac{1}{2}} \\ {-1} & {P=\frac{1}{2}}\end{array}\right.\right.$

Parameters:shape (list or tuple) – shape of Bernoulli observation matrix [M, N] Keyword Arguments:  verbose (bool) – display log info (default: {True}) Returns:Phi – Bernoulli observation matrix Return type:ndarray

pysparse.cs.sensing.obsmat.column_normalize(A)

pysparse.cs.sensing.obsmat.gaussian(shape, seed=None, verbose=True)

generates Gauss observation matrix

Generates M-by-N Gauss observation matrix which have gaussian distribution elements( columns are l2 normalized).

${\bm \Phi} \sim {\mathcal N}(0, \frac{1}{M})$

Parameters:shape (list or tuple) – shape of Gauss observation matrix [M, N] Keyword Arguments:  verbose (bool) – display log info (default: {True}) Returns:A – Gauss observation matrix $\bm A$. Return type:ndarray

pysparse.cs.sensing.obsmat.gaussian0(shape, seed=None, verbose=True)

generates Gauss observation matrix

Generates M-by-N Gauss observation matrix

${\bm \Phi} \sim {\mathcal N}(0, \frac{1}{M})$

Parameters:shape (list or tuple) – shape of Gauss observation matrix [M, N] Keyword Arguments:  verbose (bool) – display log info (default: {True}) Returns:Phi – Gauss observation matrix $\bm \Phi$. Return type:ndarray

pysparse.cs.sensing.obsmat.normalize(v)

pysparse.cs.sensing.obsmat.toeplitz(shape, verbose=True)

generates Toeplitz observation matrix

Generates M-by-N Toeplitz observation matrix

${\bm \Phi}_{ij} = \left[\begin{array}{ccccc}{a_{0}} & {a_{-1}} & {a_{-2}} & {\cdots} & {a_{-n+1}} \\ {a_{1}} & {a_{0}} & {a_{-1}} & {\cdots} & {a_{-n+2}} \\ {a_{2}} & {a_{1}} & {a_{0}} & {\cdots} & {a_{-n+3}} \\ {\vdots} & {\vdots} & {\vdots} & {\ddots} & {\vdots} \\ {a_{n-1}} & {a_{n-2}} & {a_{n-3}} & {\cdots} & {a_{0}}\end{array}\right]$

Parameters:shape (list or tuple) – shape of Toeplitz observation matrix [M, N] Keyword Arguments:  verbose (bool) – display log info (default: {True}) Returns:A – Toeplitz observation matrix $\bm A$. Return type:ndarray

Module contents