def main():
encode = np.ones((10, 10))
for i in xrange(10):
encode[i, i] = 0.9
print " start reading train data : ", time.time()
traindata_path = os.path.join(datadir, "train.csv")
traindata = LoadData.getCSV(traindata_path, np.float)
h_matrix = traindata[:, 1:784]
print time.time()
np.save("h_matrix.npy", h_matrix)
print " finish reading train data : ", time.time()
print h_matrix.shape
target = traindata[:, 0]
target_s = [encode[i] for i in target]
target = np.array(target_s)
np.save("target.npy", target)
print "start training my model : ", time.time()
weights = ANN.BackPropagation(h_matrix / 255.0, target, 2, rate=0.3, iter_times=100)
print "train finished : ", time.time()
np.save("weights.npy", weights)
print " start reading test data : ", time.time()
testdata_path = os.path.join(datadir, "test.csv")
testdata = LoadData.getCSV(testdata_path, np.float)
test_h_matrix = testdata[:, 1:784]
print "start predicting : ", time.time()
result = ANN.get_predict(test_h_matrix, weights, 784, 2, 10)
print "finish predicting : ", time.time()
np.save("result.npy", result)
def testANNPerceptron():
init_weights = np.array([0.0, 0.0, 0.0])
data = LoadData.getCSV("xor.csv", np.float, firstline=False)
target = data[:, data.shape[1] - 1]
h_matrix = data[:, :data.shape[1] - 1]
weights, residual = ANN.PerceptronRule(
h_matrix, target, init_weights, rate=0.2, iter_times=100, perceptron=True)
print weights
print residual
def readTestData():
testdata_path = os.path.join(datadir, "test.csv")
testdata = LoadData.getCSV(testdata_path, np.float)
test_h_matrix = testdata[:, 1:784]
np.save("testdata.npy", test_h_matrix)
