pub fn read_and_normalize(num_training_examples: usize) -> Result<(Vec<Sample>, Vec<Sample>)> {
    let test_rdr = Reader::from_path("./data/mnist_test.csv")?;
    let train_rdr = Reader::from_path("./data/mnist_train.csv")?;

    // closure that takes a reader and produces a vector of samples
    let read = |mut rdr: Reader<File>| -> Vec<Sample> {
        rdr.records()
            .take(num_training_examples)
            .map(|sample| {
                let sample = sample.unwrap();

                /* Create target vector */
                // Create the target vector, initialized to 10 0.1's
                let mut target = vec![0.1f64; 10];
                // The first item in the record is the label for the sample
                let label = sample[0].parse::<usize>().unwrap();
                // Change the value in the target vector at the index of the label to 0.9
                target[label] = 0.9;
                let target = DVector::from_vec(target);

                /* Create input vector */
                let input = DVector::from_vec(
                    // Start with a 1.0 as the bias for the input into the hidden layer
                    vec![1.0]
                        .into_iter()
                        // Chain the rest of the data onto this vector to create the input
                        .chain(
                            sample
                                .iter()
                                // Skip the first value because that is the label
                                .skip(1)
                                // Normalize the byte values into floats by dividing by 255.0
                                .map(|string| string.parse::<usize>().unwrap() as f64 / 255.0),
                        )
                        .collect(),
                );
                Sample {
                    label,
                    input,
                    target,
                }
            })
            .collect()
    };

    Ok((read(train_rdr), read(test_rdr)))
}
/* Loads the mnist data from the csv and normalizes the data to be between 0 and 1
 * Returns a tuple, first element is the training set and the second is the test set */
Loads the mnist data from the csv and normalizes the data to be between 0 and 1
Returns a tuple, first element is the training set and the second is the test set
Loads the mnist data from the csv and normalizes the data to be between 0 and 1 Returns a tuple first element is the training set and the second is the test set
pub fn read_and_normalize(num_training_examples: usize) -> Result<(Vec<Sample>, Vec<Sample>)> {
    let test_rdr = Reader::from_path("./data/mnist_test.csv")?;
    let train_rdr = Reader::from_path("./data/mnist_train.csv")?;
    // closure that takes a reader and produces a vector of samples
    let read = |mut rdr: Reader<File>| -> Vec<Sample> {
        rdr.records()
            .take(num_training_examples)
            .map(|sample| {
                let sample = sample.unwrap();
                /* Create target vector */
                // Create the target vector, initialized to 10 0.1's
                let mut target = vec![0.1f64; 10];
                // The first item in the record is the label for the sample
                let label = sample[0].parse::<usize>().unwrap();
                // Change the value in the target vector at the index of the label to 0.9
                target[label] = 0.9;
                let target = DVector::from_vec(target);
                /* Create input vector */
                let input = DVector::from_vec(
                    // Start with a 1.0 as the bias for the input into the hidden layer
                    vec![1.0]
                        .into_iter()
                        // Chain the rest of the data onto this vector to create the input
                        .chain(
                            sample
                                .iter()
                                // Skip the first value because that is the label
                                .skip(1)
                                // Normalize the byte values into floats by dividing by 255.0
                                .map(|string| string.parse::<usize>().unwrap() as f64 / 255.0),
                        )
                        .collect(),
                );
                Sample {
                    label,
                    input,
                    target,
                }
            })
            .collect()
    };
    Ok((read(train_rdr), read(test_rdr)))
}
pub fn read_and_normalize ( num_training_examples : usize ) -> Result < ( Vec < Sample > , Vec < Sample > ) > { let test_rdr = Reader :: from_path ( " " ) ? ; let train_rdr = Reader :: from_path ( " " ) ? ; // closure that takes a reader and produces a vector of samples let read = | mut rdr : Reader < File > | -> Vec < Sample > { rdr . records ( ) . take ( num_training_examples ) . map ( | sample | { let sample = sample . unwrap ( ) ; /* Create target vector */ // Create the target vector, initialized to 10 0.1's let mut target = vec ! [ 0.1f64 10 ] ; // The first item in the record is the label for the sample let label = sample [ 0 ] . parse :: < usize > ( ) . unwrap ( ) ; // Change the value in the target vector at the index of the label to 0.9 target [ label ] = 0.9 ; let target = DVector :: from_vec ( target ) ; /* Create input vector */ let input = DVector :: from_vec ( // Start with a 1.0 as the bias for the input into the hidden layer vec ! [ 1.0 ] . into_iter ( ) // Chain the rest of the data onto this vector to create the input . chain ( sample . iter ( ) // Skip the first value because that is the label . skip ( 1 ) // Normalize the byte values into floats by dividing by 255.0 . map ( | string | string . parse :: < usize > ( ) . unwrap ( ) as f64 / 255.0 ) , ) . collect ( ) , ) ; Sample { label , input , target , } } ) . collect ( ) } ; Ok ( ( read ( train_rdr ) , read ( test_rdr ) ) ) }
Loads the mnist data from the csv and normalizes the data to be between 0 and 1
 Returns a tuple, first element is the training set and the second is the test set
Loads the mnist data from the csv and normalizes the data to be between 0 and 1 Returns a tuple first element is the training set and the second is the test set
num_training_examples
usize
num_training_examples
usize