minnaertPredict: minnaertPredict

In ChaddFrasier/lunarLearningAlgorithms: lunar learning algorithms

Description

this function runs the prediction function for minnaert on a training set and a single test set

Usage

minnaertPredict(X.mat, Y.vec, testx.mat, y_observations)

Arguments

X.mat	input feature matrix [nx3]
Y.vec	input label vector [n]
testx.mat	test features [yx3]
y_observations	amount of test returns [y]

Value

vector of side y that contains guesses for each test observation

Examples

Hard coded example
------------------

hard_incidence <- as.numeric(c(84.106289446623, 55.328513822534, 38.106289446623, 45.345737285, 24.324, 30.324))
hard_emission <- as.numeric(c(38.288719431206, 0.13392142633291, 4.573728543534, 23.737285372, 54.345, 14.4))
hard_phase <- as.numeric(c(120.59515694473, 55.329415379509, 100.1339214473, 154.9728542, 55.57, 47.00))

hard_test_i <- c(45.345, 30.324, .0323163)
hard_test_e <- c(33.372, 54.345, 14.4)
hard_test_p <- c(54.942, 21.57, 22.00)

makeshiftX.mat <- as.matrix(data.frame(hard_incidence, hard_emission, hard_phase))
makeshiftY.vec <- as.numeric(c(0.0607816, 0.078306 , 0.098325, 0.052368, .0163620, 0.0757853))
makeshiftTestX.mat <- as.matrix(data.frame(hard_test_i, hard_test_e, hard_test_p))
predictions <- minnaertPredict(makeshiftX.mat, makeshiftY.vec, makeshiftTestX.mat, nrow(makeshiftTestX.mat))
head(predictions)

ChaddFrasier/lunarLearningAlgorithms documentation built on May 17, 2020, 6:50 p.m.