NumPy-neural-net-4.py

class scaler:
    def __init__(self, mean, std):
        self.mean = mean
        self.std = std

def get_scaler(row):
    mean = np.mean(row)
    std = np.std(row)
    return scaler(mean, std)

def standardize(data, scaler):
        return (data - scaler.mean) / scaler.std

def unstandardize(data, scaler):
    return (data * scaler.std) + scaler.mean


# Construct scalers from training set

X_scalers = [get_scaler(X_raw_train[row,:]) for row in range(X_num_row)]
X_train = np.array([standardize(X_raw_train[row,:], X_scalers[row]) for row in range(X_num_row)])

y_scalers = [get_scaler(y_raw_train[row,:]) for row in range(y_num_row)]
y_train = np.array([standardize(y_raw_train[row,:], y_scalers[row]) for row in range(y_num_row)])


# Apply those scalers to testing set

X_test = np.array([standardize(X_raw_test[row,:], X_scalers[row]) for row in range(X_num_row)])
y_test = np.array([standardize(y_raw_test[row,:], y_scalers[row]) for row in range(y_num_row)])


# Check if data has been standardized

print([X_train[row,:].mean() for row in range(X_num_row)]) # should be close to zero
print([X_train[row,:].std() for row in range(X_num_row)])  # should be close to one

print([y_train[row,:].mean() for row in range(y_num_row)]) # should be close to zero
print([y_train[row,:].std() for row in range(y_num_row)])  # should be close to one