CMSIS_5/CMSIS/DSP/Testing/PatternGeneration/Support.py

import os.path
import itertools
import Tools
import random
import numpy as np

NBTESTSAMPLES = 10

# Nb vectors for barycenter
NBVECTORS = [4,10,16]

VECDIM = [12,14,20]

def genWsum(config,nb,vecDim):
    dims=[] 
    inputs=[] 
    weights=[]
    output=[] 


    va = np.random.rand(vecDim)
    vb = np.random.rand(vecDim)
    e = np.sum(va.T * vb) / np.sum(vb) 
    inputs += list(va)
    weights += list(vb)
    output.append(e)
    inputs=np.array(inputs)
    weights=np.array(weights)
    output=np.array(output)
    config.writeInput(nb, inputs,"Inputs")
    config.writeInput(nb, weights,"Weights")
    config.writeReference(nb, output,"Ref")

def genBarycenter(config,nb,nbTests,nbVecs,vecDim):
    dims=[] 
    inputs=[] 
    weights=[]
    output=[] 

    dims.append(nbTests)
    dims.append(nbVecs)
    dims.append(vecDim)

    for _ in range(0,nbTests):
      
      vecs = []
      b = np.zeros(vecDim)
      coefs = np.random.rand(nbVecs)
  
      for i in range(nbVecs):
            va = np.random.rand(vecDim)
            b += va * coefs[i]
            vecs += list(va)
                     
      b = b / np.sum(coefs)
     
      inputs += list(vecs)
      weights += list(coefs)
      output += list(b)
    inputs=np.array(inputs)
    weights=np.array(weights)
    output=np.array(output)
    config.writeInput(nb, inputs,"Inputs")
    config.writeInputS16(nb, dims,"Dims")
    config.writeInput(nb, weights,"Weights")
    config.writeReference(nb, output,"Ref")

def writeTestsF32(config):
    NBSAMPLES=256

    va = np.random.rand(NBSAMPLES)
    config.writeInput(1,va,"Samples")

    config.writeInputQ15(3,va,"Samples")
    config.writeInputQ31(4,va,"Samples")
    config.writeInputQ7(5,va,"Samples")

    # This is for benchmarking the weighted sum and we use only one test pattern
    genWsum(config,6,256)
    


def writeTestsQ31(config):
    NBSAMPLES=256

    va = np.random.rand(NBSAMPLES)
    config.writeInput(1,va,"Samples")

    config.writeInputQ15(3,va,"Samples")
    config.writeInputQ7(4,va,"Samples")


def writeTestsQ15(config):
    NBSAMPLES=256

    va = np.random.rand(NBSAMPLES)
    config.writeInput(1,va,"Samples")

    config.writeInputQ31(3,va,"Samples")
    config.writeInputQ7(4,va,"Samples")

def writeTestsQ7(config):
    NBSAMPLES=256

    va = np.random.rand(NBSAMPLES)
    config.writeInput(1,va,"Samples")

    config.writeInputQ31(3,va,"Samples")
    config.writeInputQ15(4,va,"Samples")

def writeBarTests(config):
    # For testing
    genBarycenter(config,1,NBTESTSAMPLES,10,14)

    # For benchmarks
    va = np.random.rand(128*15)
    vb = np.random.rand(128)
    config.writeInput(1,va,"Samples")
    config.writeInput(1,vb,"Coefs")


PATTERNDIR = os.path.join("Patterns","DSP","Support","Support")
PARAMDIR = os.path.join("Parameters","DSP","Support","Support")

configf32=Tools.Config(PATTERNDIR,PARAMDIR,"f32")
configq31=Tools.Config(PATTERNDIR,PARAMDIR,"q31")
configq15=Tools.Config(PATTERNDIR,PARAMDIR,"q15")
configq7=Tools.Config(PATTERNDIR,PARAMDIR,"q7")

writeTestsF32(configf32)
writeTestsQ31(configq31)
writeTestsQ15(configq15)
writeTestsQ7(configq7)


# For benchmarking we need to vary number of vectors and vector dimension separately
PATTERNBARDIR = os.path.join("Patterns","DSP","SupportBar")
PARAMBARDIR = os.path.join("Parameters","DSP","SupportBar")

configBarf32=Tools.Config(PATTERNBARDIR,PARAMBARDIR,"f32")

writeBarTests(configBarf32)