Cope.experimental.linalg Namespace Reference

Classes
class	Space

Functions
def	matrix (string, rows=None, cols=None, cp=False, np=False, immutable=False, verbose=False)
def	combineMatricies (*mats)
def	columnspace (M)
def	eigenspaces (mat)
def	convert2Equs (mat, vars)
def	isSimilar (*args)
def	steadyState (stochastic, accuracy=10000000000000, verbose=False)
def	randMarkovState (rows, balanced=True, np=False)
def	isOrthogonal (*vects, innerProduct=None)
def	vectorLength (v)
def	EuclideanDist (u, v)
def	manhattanDist (a, b)
def	minkowskiDist (a, b, p)
def	orthonormalize (orthogonalSet)
list	splitVector ('Matrix' y, 'Space' W, innerProduct=None)
'(vector in W, vector in W⟂)'	project ('Matrix' y, 'Space' W, innerProduct=None)
def	normalizePercentage (p, error='Percentage is of the wrong type(int or float expected)')

Function Documentation

columnspace()

def Cope.experimental.linalg.columnspace

(

M

)

combineMatricies()

def Cope.experimental.linalg.combineMatricies

(

*

mats

)

convert2Equs()

def Cope.experimental.linalg.convert2Equs	(		mat,
			vars
	)

 Converts a matrix into the list of equations it represents 

eigenspaces()

def Cope.experimental.linalg.eigenspaces

(

mat

)

EuclideanDist()

def Cope.experimental.linalg.EuclideanDist	(		u,
			v
	)

isOrthogonal()

def Cope.experimental.linalg.isOrthogonal	(	*	vects,
			innerProduct = None
	)

isSimilar()

def Cope.experimental.linalg.isSimilar

(

*

args

)

manhattanDist()

def Cope.experimental.linalg.manhattanDist	(		a,
			b
	)

matrix()

def Cope.experimental.linalg.matrix	(		string,
			rows = None,
			cols = None,
			cp = False,
			np = False,
			immutable = False,
			verbose = False
	)

minkowskiDist()

def Cope.experimental.linalg.minkowskiDist	(		a,
			b,
			p
	)

normalizePercentage()

def Cope.experimental.linalg.normalizePercentage	(		p,
			error = 'Percentage is of the wrong type (int or float expected)'
	)

orthonormalize()

def Cope.experimental.linalg.orthonormalize

(

orthogonalSet

)

project()

'(vector in W, vector in W⟂)' Cope.experimental.linalg.project	(	'Matrix'	y,
		'Space'	W,
			innerProduct = None
	)

 W  = a subspace of R^n we want to describe y with (to get proj_y)
    y  = some y in R^n
    proj_y = a vector in W
    z  = a vector in W⟂ (W perp) 

randMarkovState()

def Cope.experimental.linalg.randMarkovState	(		rows,
			balanced = True,
			np = False
	)

splitVector()

list Cope.experimental.linalg.splitVector	(	'Matrix'	y,
		'Space'	W,
			innerProduct = None
	)

 Returns vectors in W which can be linearly combined to get y 

steadyState()

def Cope.experimental.linalg.steadyState	(		stochastic,
			accuracy = 10000000000000,
			verbose = False
	)

vectorLength()

def Cope.experimental.linalg.vectorLength

(

v

)