Cope 2.5.0
My personal "standard library" of all the generally useful code I've written for various projects over the years
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Classes | Functions
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)