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class MinimalChain():
    def __init__(self): # initialize when creating a chain
        self.blocks = [self.get_genesis_block()]
    
    def get_genesis_block(self): 
        return MinimalBlock(0, 
                            datetime.datetime.utcnow(), 
                            'Genesis', 
                            'arbitrary')
    

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import hashlib

class MinimalBlock():
    def __init__(self, index, timestamp, data, previous_hash):
        self.index = index
        self.timestamp = timestamp
        self.data = data
        self.previous_hash = previous_hash
        self.hash = self.hashing()
    

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# test loss

print(get_loss(y_test, forward_prop(X_test)))

def predict(X_raw_any):
    X_any = np.array([standardize(X_raw_any[row,:], X_scalers[row]) for row in range(X_num_row)])
    y_hat = forward_prop(X_any)
    y_hat_any = np.array([unstandardize(y_hat[row,:], y_scalers[row]) for row in range(y_num_row)])
    return y_hat_any
    

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learning_rate = 0.01
max_epoch = 1000000

for epoch in range(1,max_epoch+1):
    y_hat_train = forward_prop(X_train) # update y_hat
    backward_prop(y_train, y_hat_train) # update (dW,db)
    
    for layer_index in range(1,len(layers_dim)):        # update (W,b)
        neural_net[layer_index].W = neural_net[layer_index].W - learning_rate * neural_net[layer_index].dW
        neural_net[layer_index].b = neural_net[layer_index].b - learning_rate * neural_net[layer_index].db

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def get_loss(y, y_hat, metric='mse'):
    if metric == 'mse':
        individual_loss = 0.5 * (y_hat - y) ** 2
        return np.mean([np.linalg.norm(individual_loss[:,col], 2) for col in range(individual_loss.shape[1])])
    else:
        raise Exception('Loss metric is not defined.')

def get_dZ_from_loss(y, y_hat, metric):
    if metric == 'mse':
        return y_hat - y

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def activation(input_, act_func):
    if act_func == 'relu':
        return np.maximum(input_, np.zeros(input_.shape))
    elif act_func == 'linear':
        return input_
    else:
        raise Exception('Activation function is not defined.')
        
  
def forward_prop(input_vec, layers_dim=layers_dim, neural_net=neural_net):

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class layer:
    def __init__(self, layer_index, is_output, input_dim, output_dim, activation):
        self.layer_index = layer_index # zero indicates input layer
        self.is_output = is_output # true indicates output layer, false otherwise
        self.input_dim = input_dim
        self.output_dim = output_dim
        self.activation = activation
        
        # the multiplication constant is sorta arbitrary
        if layer_index != 0:

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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)

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train_ratio = 0.7
num_train_datum = int(train_ratio*X_num_col)
X_raw_train = X_raw[:,0:num_train_datum]
X_raw_test = X_raw[:,num_train_datum:]

y_raw_train = y_raw[:,0:num_train_datum]
y_raw_test = y_raw[:,num_train_datum:]

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X_num_row, X_num_col = [2, 10000] # Row is no. of feature, col is no. of datum points
X_raw = np.random.rand(X_num_row,X_num_col) * 100

y_raw = np.concatenate(([(X_raw[0,:] + X_raw[1,:])], [(X_raw[0,:] - X_raw[1,:])], np.abs([(X_raw[0,:] - X_raw[1,:])])))
# for input a and b, output is a+b; a-b and |a-b|
y_num_row, y_num_col = y_raw.shape