README.md

In ghouljd/apnnClassifier: Multiclass classifier based on neural networks.

R package - apnnClassifier

R package that classifies using probabilistic neural networks and evaluate the results.

Functions

1. standardize

It is responsible for standardizing a set of data.

Params

• set: Data set. (Required).
• type: standardization type (Not required. Default value: "punctual". Valid values: "punctual" - "scale").

Return

⋅⋅⋅ standardized set.

Example

library(apnnClassifier)
data(testData)
Basic usage
testData <- standardize(testData)
For classification by minimum and maximum
testData <- standardize(testData, type = "scale")
View(testData)

2. trainNeuralNet

It is responsible for executing the prediction of the probabilistic neural network.

Params

• train_set: Training set. (Required).
• test_set: Testing set. (Required).
• category_column: Category column (Not required. Default value: 1).
• sigma: Optimum value for the activation function of the neural network. (Not required).

Return

⋅⋅⋅ pnn trained with the classified testing set and network performance statistics.

Example

library(apnnClassifier)
data(trainData, testData)
# Basic usage.
pnn <- trainNeuralNet(train_set = trainData, test_set = testData)
# If you know the approximate optimal value and the sorter column is not in the first position of the set.
pnn <- trainNeuralNet(train_set = trainData, test_set = testData, category_column =  *sorter column*, sigma = *sigma value*)
View(pnn)

3. evaluate

Responsible of analyze the classification of the probabilistic neural network and generating the corresponding analysis graphs.

Params

• pnn: Trained probabilistic neural network.(Required)

Return

⋅⋅⋅ pnn with evaluated input and analysis charts.

Example

library(apnnClassifier)
data(trainData, testData)
pnn <- trainNeuralNet(train_set = trainData, test_set = testData, sigma = 0.5)
pnn <- evaluate(pnn)
View(pnn)

ghouljd/apnnClassifier documentation built on June 29, 2019, 11:38 p.m.