def initExperiment(_args):
FileHandler().createFolder("results")
resultFolder = "results/" + args.name + "/"
FileHandler().createFolder(resultFolder)
resultFile = resultFolder + "result.csv"
if _args.classification:
e = Experiment(args.classification, args.name)
models = initModels(_args, Type.CLASSIFICATION)
e.classification(models, 10)
if _args.gen_code:
exportCode(_args, resultFolder, _args.classification, models)
if _args.visualize:
files = [e.path("cv_" + str(i) + ".csv") for i in range(len(models))]
xTicks = [model.modelName for model in models]
ResultVisualizer().boxplots(files, _args.visualize, xTicks, ylabel=_args.visualize)
elif _args.correlation:
csv = CSV()
csv.load(args.correlation)
csv.computeCorrelationMatrix(resultFile)
if _args.visualize:
ResultVisualizer().colorMap(resultFile)
elif _args.regression:
e = Experiment(args.regression, args.name)
models = initModels(_args, Type.REGRESSION)
e.regression(models, 10)
if _args.gen_code:
exportCode(_args, resultFolder, _args.regression, models)
if _args.visualize:
files = [e.path("cv_" + str(i) + ".csv") for i in range(len(models))]
xTicks = [model.modelName for model in models]
ResultVisualizer().boxplots(files, _args.visualize, xTicks, ylabel=_args.visualize)
print("[LIMITS]: results written to src/" + resultFolder)
def classificationANN(_training, _layers, _nodes, _file):
e = Experiment(_training, verbose=False)
R = ResultMatrix()
for numLayers in range(1, _layers + 1):
for numNodes in range(1, _nodes + 1):
ann = ANN()
ann.config.hiddenLayers = []
for i in range(numLayers):
ann.config.hiddenLayers.append(numNodes)
header, result = e.classification([ann], 10)
mem = computeMemorySize(_training, ann, False)
header += ["arduino", "msp", "esp"]
result = np.hstack([result, mem])
print([
"#layers=" + str(numLayers) + "/" + str(_layers) + " nodes=" +
str(numNodes) + "/" + str(_nodes) + ' mem=', mem
],
flush=True)
R.add(header, result)
R.save(_file)
from data.CSV import CSV
# define the training data set and set up the model
training = "../examples/mnoA.csv"
training = "../examples/vehicleClassification.csv"
csv = CSV(training)
attributes = csv.findAttributes(0)
d = csv.discretizeData()
model = RandomForest()
model.config.trees = 10
model.config.depth = 5
# perform a 10-fold cross validation
e = Experiment(training, "example_rf_disc")
e.classification([model], 10)
# export the C++ code
CodeGenerator().export(training, model, e.path("rf.cpp"), d)
#
ce = CodeEvaluator()
R, C = ce.crossValidation(model, training, attributes, e.tmp(), d)
R.printAggregated()
# all results are written to results/example_rf_disc/