def main(argv=None):
if argv is None:
argv = sys.argv[
1:] #dirty trick to make this convenient in the interpreter.
if (len(argv) < 3 or len(argv) > 4):
print(("bad number of args:", len(argv)))
print(usage)
else:
if (len(argv) == 3):
exprs, keys = SymbolData.unpickleSymbols(argv[2])
else:
exprs = SymbolData.readInkmlDirectory(argv[2], argv[3], True)
keys = SymbolData.defaultClasses
if (argv[0] == "-nn"):
model = Classification.OneNN()
elif (argv[0] == "-rf"):
model = Classification.makeRF()
elif (argv[0] == "-et"):
model = Classification.makeET()
else:
with open(argv[0], 'rb') as f:
model = pickle.load(f)
#this had better actually be a sklearn model or the equivelent.
#things will break in ways that are hard for me to test for if it isn't.
symbs = SymbolData.allSymbols(exprs)
trained, pca = Classification.train(model, symbs, keys)
print("Done training.")
if False:
f = Features.features(symbs)
if (pca != None):
f = pca.transform(f)
pred = model.predict(f)
print("Accuracy on training set : ",
accuracy_score(SymbolData.classNumbers(symbs, keys), pred))
#joblib.dump((trained, pca), argv[2])
with open(argv[1], 'wb') as f:
pickle.dump((trained, pca, keys), f, pickle.HIGHEST_PROTOCOL)
