iters += 1
return x_proc, errors
if __name__ == "__main__":
random.seed(8)
numStates = 25
numFeatures = 10
filesLocation = "Outputs/imSaves/RMSProp/"
print(
"Error LeastSq,Error TP, Error LstSq - Error TP,Iterations for lower error than Least Squares"
)
for iter in range(10):
A = generator.GenerateRandomMatrix(numStates, numFeatures, max=10)
b = generator.GenerateRandomVector(numStates)
start_x = generator.GenerateRandomVector(numFeatures)
probDist = 1 / numStates * np.ones(numStates)
errorType = 8
maxIters = 5000
np.set_printoptions(precision=3)
lstSq = la.inv(A.T @ A) @ (A.T @ b)
lstSqError = errorCalcs.getErrorMethod(A,
b,
lstSq,
probDist,
errorType,
norm=2)
momentumChoices = [0.5]
def runChoices(numberofRunsChoice, maxIterationsChoice, stochasticTypeChoice,
sizeofLinearSystemChoice, randomInputsTypeChoice,
stepSizeTypeChoice, momentumTypeChoice, momentumParamChoice,
momentumParam2Choice):
# We create lists for possible choices below:
numberofRunsQues, numberofRunsList, maxIterationsQues, maxIterationsList, stochasticTypeQues, stochasticTypeList, \
sizeofLinearSystemQues, sizeofLinearSystemList, randomInputTypeQues, randomInputTypeList, \
stepSizeTypeQues, stepSizeTypeList, momentumTypeQues, momentumTypeList, \
momentumParamQues, momentumParamList, momentumParam2Ques, momentumParam2List = interface.choicesData()
# We retrieve all user inputs
numberofRuns = numberofRunsList[numberofRunsChoice - 1]
maxIterations = maxIterationsList[maxIterationsChoice - 1]
stochasticType = stochasticTypeList[stochasticTypeChoice - 1]
sizeofLinearSystem = sizeofLinearSystemList[sizeofLinearSystemChoice - 1]
randomInputType = randomInputTypeList[randomInputsTypeChoice - 1]
stepSizeType = stepSizeTypeList[stepSizeTypeChoice - 1]
momentumType = momentumTypeList[momentumTypeChoice - 1]
momentumParam = momentumParamList[momentumParamChoice - 1]
momentumParam2 = momentumParam2List[momentumParam2Choice - 1]
# We get the size of the system
numStates, numFeatures = getSizeofLinearSystemFromSizeString(
sizeofLinearSystem)
# We use seed so that all files generated use same seed for repeatability
random.seed(8)
# Below is the location where the log files and charts generated will be stored
filesLocation = "Outputs/runAllOutputs/"
simName = getSimName(sizeofLinearSystem, stochasticType, randomInputType,
stepSizeType, momentumType, momentumTypeChoice,
momentumParam, momentumParam2)
logFileName = simName + "_Log.txt"
# We now create the file
f = open(filesLocation + logFileName, "w")
f.close()
errorType = 8
# We set some basic variables below
maxIters = maxIterations
totalErrors, meanErrors = np.zeros(maxIters), np.zeros(maxIters)
totalIterations = numberofRuns
# We loop for each iteration below
for iter in range(totalIterations):
# We generate the required matrices
A, b = generateAbasPerChoiceInputs(randomInputsTypeChoice, numStates,
numFeatures)
start_x = generator.GenerateRandomVector(numFeatures)
# This is the transition probability matrix, now set to uniform
probDist = 1 / numStates * np.ones(numStates)
# We set the print rules
np.set_printoptions(precision=3)
# This is the main caller. It calls CodeRunner from oneRing.py which retrieves all errors for choices.
x_out, errors = oneRing.CodeRunner(A,
b,
start_x,
probDist,
maxIters,
errorType=errorType,
stochasticType=stochasticType,
stepSizeType=stepSizeType,
momentumType=momentumType,
momentumParam=momentumParam,
momentumParam2=momentumParam2)
# We now write these errors to file.
f = open(filesLocation + logFileName, "a")
f.write("Errors for iteration: %d\n" % (iter + 1))
for i in range(len(errors)):
f.write("%d,%.2e\n" % (i + 1, errors[i]))
f.close()
# We compute the total error so far
totalErrors = totalErrors + errors
# We plot the errors into chart
chartTitle = getChartName(sizeofLinearSystem, stochasticType,
randomInputType, stepSizeType, momentumType,
momentumTypeChoice, momentumParam,
momentumParam2)
chartFileName = str(iter + 1) + "-" + simName
DrawingCharting.drawSavePlot(maxIters,
errors,
chartTitle,
label='Total Projections Error',
color="mediumorchid",
logErrors=True,
plotShow=False,
location=filesLocation,
fileName=chartFileName + ".png")
# We now calculate the mean error over all the iterations
meanErrors = totalErrors / totalIterations
# We paste these mean errors into the log file
f = open(filesLocation + logFileName, "a")
f.write("Average Errors\n")
for i in range(len(meanErrors)):
f.write("%d,%.2e\n" % (i + 1, meanErrors[i]))
f.close()