def get_pybrain_data_set(data, input_cols, target_cols=1):
data_set = SupervisedDataSet(input_cols, target_cols)
for row in data:
# get first X columns for inputs
# (technically "all indices less than X")
# get last column as target
data_set.addSample(tuple(row[:input_cols]), tuple([row[input_cols]]))
return data_set
# normalize all values
p['wti_var'] = normalize(p['wti_var'])
p['wti_skew'] = normalize(p['wti_skew'])
p['wti_curt'] = normalize(p['wti_curt'])
p['i_entr'] = normalize(p['i_entr'])
# shuffle up data
p.reindex(np.random.permutation(p.index))
trainer = BackpropTrainer(
buildNetwork(4, 5, 1), # 2 input nodes, 3 hidden nodes, 1 output node
get_pybrain_data_set(p.as_matrix(), 4),
verbose=True)
#print(trainer.train())
#evaluator = ModuleValidator.classificationPerformance(trainer.module, trainer.ds)
cv = CrossValidator(trainer, trainer.ds, n_folds=5)
cv.setArgs(max_epochs=2, verbose=True)
print(cv.validate())
'o': 1,
'b': 2,
}
def row_preprocess(row):
return [translation[x] for x in row]
if __name__ == "__main__":
raw_data = list(csv.reader(open("tic-tac-toe.data")))
targets = [1 if x[-1] == 'positive' else 0 for x in raw_data]
inputs = [row_preprocess(x[:-1]) for x in raw_data]
alldata = ClassificationDataSet(
9, class_labels=['negative', 'positive'])
for (i, t) in zip(inputs, targets):
alldata.addSample(i, [t])
network = buildNetwork(9, int(sys.argv[1]), 1,
hiddenclass=SigmoidLayer,
outclass=LinearLayer)
trainer = BackpropTrainer(network, weightdecay=0.001)
validator = CrossValidator(trainer, alldata, n_folds=2,
valfunc=ModuleValidator.MSE)
validator.setArgs(max_epochs=500)
ret = validator.validate()
print(ret)
