print( "Accuracy on testing set : ", accuracy_score(NP.concatenate(cors), NP.concatenate(preds)))
return (cors, preds)
def classifyExpression(expression, keys, model, pca, renormalize=True):
symbs = expression.symbols
if renormalize:
symbs = SymbolData.normalize(symbs, 99)
f = Features.features(symbs)
if (len (symbs) == 0):
print(expression.name, " has no valid symbols!")
return ([], [])
if (pca != None):
f = pca.transform(f)
pred = model.predict(f)
assert (max(pred) < len(keys))
f = (lambda p: keys[p])
expression.classes = map (f, pred)
return (NP.array(SymbolData.classNumbers(symbs, keys)), pred)
"""
symbols = SymbolData.unpickleSymbols("train.dat")
symbols = SymbolData.normalize(symbols,99)
Features.showImg(symbols[3])
f = Features.features(symbols[0:2])
"""
