mlpack_perceptron(1)

💖 mlpack_perceptron 💖

okay bestie, gather round, because today we're learning mlpack_perceptron — it's a User Command, it lives in the mlpack family, and it is literally the moment a single level neural network becomes your bestie. no cap. 🧚✨

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✨ SYNOPSIS ✨

mlpack_perceptron [-m unknown] [-l unknown] [-n int] [-T unknown] [-t unknown] [-V bool] [-M unknown] [-P unknown] [-h -v]

like, that's the whole vibe right there. every flag, all at once, main character energy. 💅

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🌸 DESCRIPTION 🌸

okay so. this program implements a perceptron, which is a single level neural network. it makes its predictions based on a linear predictor function that combines a set of weights with the feature vector. the perceptron learning rule is able to converge, given enough iterations (you specify those with the --max_iterations (-n) parameter, bestie — write that down), if the data supplied is linearly separable. the perceptron is parameterized by a matrix of weight vectors that denote the numerical weights of the neural network. it's giving math but make it cute. 🦋

this program lets you load a perceptron from a model (via the --input_model_file (-m) parameter) or train a perceptron given training data (via the --training_file (-t) parameter), or both those things at once. and also this program allows classification on a test dataset (via the --test_file (-T) parameter), and the classification results on the test set may be saved with the --predictions_file (-P) output parameter. the perceptron model may be saved with the --output_model_file (-M) output parameter. iconique. 👑

the training data you give with the --training_file (-t) option may have class labels as its last dimension (so, if the training data is in CSV format, labels should be the last column, fr fr). alternately, the --labels_file (-l) parameter may be used to specify a separate matrix of labels. your call, babe. 💜

all these options make it easy to train a perceptron, and then re-use that perceptron for later classification. the invocation below trains a perceptron on training_data.csv with labels training_labels.csv, and saves the model to perceptron_model.bin:

$ mlpack_perceptron --training_file training_data.csv --labels_file training_labels.csv --output_model_file perceptron_model.bin

then, this model can be re-used for classification on the test data test_data.csv. the example below does precisely that, saving the predicted classes to predictions.csv:

$ mlpack_perceptron --input_model_file perceptron_model.bin --test_file test_data.csv --predictions_file predictions.csv

slay. 🎀

now listen, note that all of the options may be specified at once: predictions may be calculated right after training a model, and model training can occur even if an existing perceptron model is passed with the --input_model_file (-m) parameter. BUT. ⚠️ note that the number of classes and the dimensionality of all data must match. so you cannot pass a perceptron model trained on 2 classes and then re-train with a 4-class dataset. similarly, attempting classification on a 3-dimensional dataset with a perceptron that has been trained on 8 dimensions will cause an error. like, it will simply not be the vibe. 😭

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🎀 OPTIONAL INPUT OPTIONS 🎀

• --help (-h) [bool] — default help info. the classic "help me" button, and honestly? same.
• --info [string] — print help on a specific option. default value ''. so you can be like "tell me about ONE thing" and it will. so specific, we love her. ✨
• --input_model_file (-m) [unknown] — input perceptron model.
• --labels_file (-l) [unknown] — a matrix containing labels for the training set.
• --max_iterations (-n) [int] — the maximum number of iterations the perceptron is to be run. default value 1000. yes bestie, one thousand, that's the patience level. 💅
• --test_file (-T) [unknown] — a matrix containing the test set.
• --training_file (-t) [unknown] — a matrix containing the training set.
• --verbose (-v) [bool] — display informational messages and the full list of parameters and timers at the end of execution. she is chatty and we respect it. 🗣️
• --version (-V) [bool] — display the version of mlpack. know your roots, queen. 👑

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💖 OPTIONAL OUTPUT OPTIONS 💖

• --output_model_file (-M) [unknown] — output for trained perceptron model.
• --predictions_file (-P) [unknown] — the matrix in which the predicted labels for the test set will be written. the receipts. the results. the moment. 🍓

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🌈 ADDITIONAL INFORMATION 🌈

for further information, including relevant papers, citations, and theory, consult the documentation found at http://www.mlpack.org or included with your distribution of mlpack. like, go read, i promise it eats. 📚

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okay that's the whole page bestie, you're officially a perceptron girlie now 🌸🧚✨ stay linearly separable and hydrated 💖 this has been mlpack_perceptron, part of mlpack-4.8.0, dated 22 June 2026 — periodt. 💅🫶