Passed fmt

datasets/imdb.v

@@ -3,11 +3,9 @@ module datasets
 import vtl
 import os
 
-pub const (
-	imdb_folder_name = 'aclImdb'
-	imdb_file_name   = '${imdb_folder_name}_v1.tar.gz'
-	imdb_base_url    = 'http://ai.stanford.edu/~amaas/data/sentiment/'
-)
+pub const imdb_folder_name = 'aclImdb'
+pub const imdb_file_name = '${imdb_folder_name}_v1.tar.gz'
+pub const imdb_base_url = 'http://ai.stanford.edu/~amaas/data/sentiment/'
 
 // ImdbDataset is a dataset for sentiment analysis.
 pub struct ImdbDataset {

datasets/mnist.v

@@ -3,13 +3,11 @@ module datasets
 import vtl
 import os
 
-pub const (
-	mnist_base_url          = 'http://yann.lecun.com/exdb/mnist/'
-	mnist_train_images_file = 'train-images-idx3-ubyte.gz'
-	mnist_train_labels_file = 'train-labels-idx1-ubyte.gz'
-	mnist_test_images_file  = 't10k-images-idx3-ubyte.gz'
-	mnist_test_labels_file  = 't10k-labels-idx1-ubyte.gz'
-)
+pub const mnist_base_url = 'http://yann.lecun.com/exdb/mnist/'
+pub const mnist_train_images_file = 'train-images-idx3-ubyte.gz'
+pub const mnist_train_labels_file = 'train-labels-idx1-ubyte.gz'
+pub const mnist_test_images_file = 't10k-images-idx3-ubyte.gz'
+pub const mnist_test_labels_file = 't10k-labels-idx1-ubyte.gz'
 
 // MnistDataset is a dataset of MNIST handwritten digits.
 pub struct MnistDataset {

examples/nn_mnist/main.v

@@ -6,11 +6,9 @@ import vtl.datasets
 import vtl.nn.models
 import vtl.nn.optimizers
 
-const (
-	batch_size = 28
-	epochs     = 1
-	batches    = 100
-)
+const batch_size = 28
+const epochs = 1
+const batches = 100
 
 fn main() {
 	// Autograd context / neuralnet graph

examples/nn_simple_two_layer/main.v

@@ -5,13 +5,11 @@ import vtl.autograd
 import vtl.nn.models
 import vtl.nn.optimizers
 
-const (
-	batch_size = 64
-	input_dim  = 1000
-	hidden_dim = 100
-	output_dim = 10
-	epochs     = 500
-)
+const batch_size = 64
+const input_dim = 1000
+const hidden_dim = 100
+const output_dim = 10
+const epochs = 500
 
 // Learning XOR function with a neural network.
 

examples/nn_xor/main.v

@@ -5,11 +5,9 @@ import vtl.autograd
 import vtl.nn.models
 import vtl.nn.optimizers
 
-const (
-	batch_size = 32
-	epochs     = 6
-	batches    = 100
-)
+const batch_size = 32
+const epochs = 6
+const batches = 100
 
 // Learning XOR function with a neural network.
 

storage/cpu.v

@@ -1,10 +1,8 @@
 module storage
 
-pub const (
-	vector_minimum_capacity = 2
-	vector_growth_factor    = 2
-	vector_shrink_threshold = 1.0 / 4.0
-)
+pub const vector_minimum_capacity = 2
+pub const vector_growth_factor = 2
+pub const vector_shrink_threshold = 1.0 / 4.0
 
 // CpuStorage
 @[heap]