Nothing
## ---- include = FALSE---------------------------------------------------------
LOCAL <- identical(Sys.getenv("LOCAL"), "true")
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
#knitr::opts_chunk$set(purl = CRAN)
## -----------------------------------------------------------------------------
library(biClassify)
## -----------------------------------------------------------------------------
data(LDA_Data)
## ---- echo = FALSE, fig.height=4, fig.width=5, fig.align = "center"-----------
plot(LDA_Data$TrainData[,2]~LDA_Data$TrainData[,1],
col = c("orange","blue")[LDA_Data$TrainCat],
pch = c("1","2")[LDA_Data$TrainCat],
xlab = "Feature 1",
ylab = "Feature 2",
main = "Scatter Plot of LDA Training Data")
## ---- eval = FALSE------------------------------------------------------------
# > test_pred <- LDA(TrainData = LDA_Data$TrainData,
# TrainCat = LDA_Data$TrainCat,
# TestData = LDA_Data$TestData,
# Method = "Compressed")$Predictions
#
# > mean(test_pred != LDA_Data$TestCat)
# [1] 0
## -----------------------------------------------------------------------------
data(QDA_Data)
## ---- echo = FALSE, fig.height=4, fig.width=5, fig.align = "center"-----------
plot(QDA_Data$TrainData[,2]~QDA_Data$TrainData[,1],
col = c("orange","blue")[QDA_Data$TrainCat],
pch = c("1","2")[QDA_Data$TrainCat],
xlab = "Feature 1",
ylab = "Feature 2",
main = "Scatter Plot of QDA Training Data")
## ---- eval = FALSE------------------------------------------------------------
# > test_pred <- QDA(TrainData = QDA_Data$TrainData,
# TrainCat = QDA_Data$TrainCat,
# TestData = QDA_Data$TestData,
# Method = "Compressed")
#
# > mean(test_pred != QDA_Data$TestCat)
# [1] 0
## -----------------------------------------------------------------------------
data(KOS_Data)
## ----echo = FALSE, fig.height=4, fig.width=8, fig.align = "center"------------
par(mfrow = c(1,2))
plot(KOS_Data$TrainData[,2]~KOS_Data$TrainData[,1], col = c("orange","blue")[KOS_Data$TrainCat],
pch = c("1","2")[KOS_Data$TrainCat],
xlab = "Feature 1",
ylab = "Feature 2",
main = "True Features")
plot(KOS_Data$TrainData[,4]~KOS_Data$TrainData[,3], col = c("orange","blue")[KOS_Data$TrainCat],
pch = c("1","2")[KOS_Data$TrainCat],
xlab = "Feature 3",
ylab = "Feature 4",
main = "Noise Features")
par(mfrow = c(1,1))
## ---- eval = FALSE------------------------------------------------------------
# > output <- KOS(TrainData = KOS_Data$TrainData,
# TrainCat = KOS_Data$TrainCat,
# TestData = KOS_Data$TestData)
# > output$Weight
# [1] 1 1 0 0
#
# > mean(output$Predictions != KOS_Data$TestCat)
# [1] 0
#
# > summary(output$Dvec)
# V1
# Min. :-0.03002
# 1st Qu.:-0.01953
# Median :-0.01445
# Mean : 0.00000
# 3rd Qu.: 0.03788
# Max. : 0.05799
## -----------------------------------------------------------------------------
TrainData <- LDA_Data$TrainData
TrainCat <- LDA_Data$TrainCat
TestData <- LDA_Data$TestData
TestCat <- LDA_Data$TestCat
## -----------------------------------------------------------------------------
test_pred <- LDA(TrainData, TrainCat, TestData)$Predictions
table(test_pred)
mean(test_pred != TestCat)
## -----------------------------------------------------------------------------
test_pred <- LDA(TrainData, TrainCat, TestData,
Method = "Compressed", Mode = "Automatic")$Predictions
table(test_pred)
mean(test_pred != TestCat)
## -----------------------------------------------------------------------------
test_pred <- LDA(TrainData, TrainCat, TestData, Method = "Compressed")$Predictions
table(test_pred)
mean(test_pred != TestCat)
## ---- eval=FALSE--------------------------------------------------------------
# output <- LDA(TrainData, TrainCat, TestData,
# Method = "Compressed", Mode = "Interactive")$Predictions
# "Please enter the number m1 of group 1 compression samples: "700
# "Please enter the number m2 of group 2 compression samples: "300
# "Please enter sparsity level s used in compression: "0.01
## -----------------------------------------------------------------------------
test_pred <- LDA(TrainData, TrainCat, TestData,
Method = "Compressed", Mode = "Research",
m1 = 700, m2 = 300, s = 0.01)$Predictions
table(test_pred)
mean(test_pred != TestCat)
## -----------------------------------------------------------------------------
test_pred <- LDA(TrainData, TrainCat, TestData,
Method = "Subsampled", Mode = "Automatic")$Predictions
table(test_pred)
## -----------------------------------------------------------------------------
test_pred <- LDA(TrainData, TrainCat, TestData,
Method = "Subsampled")$Predictions
table(test_pred)
## ---- eval=FALSE--------------------------------------------------------------
# test_pred <- LDA(TrainData, TrainCat, TestData,
# Method = "Subsampled", Mode = "Interactive")$Predictions
# "Please enter the number m1 of group 1 sub-samples: "700
# "Please enter the number m2 of group 2 sub-samples: "300
## -----------------------------------------------------------------------------
output <- LDA(TrainData, TrainCat, TestData,
Method = "Subsampled", Mode = "Research",
m1 = 700, m2 = 300)$Predictions
table(output)
mean(output != TestCat)
## -----------------------------------------------------------------------------
output <- LDA(TrainData, TrainCat, TestData,
Method = "Projected", Mode = "Automatic")$Predictions
table(output)
mean(output != TestCat)
## -----------------------------------------------------------------------------
output <- LDA(TrainData, TrainCat, TestData,
Method = "Projected")$Predictions
table(output)
mean(output != TestCat)
## ---- eval=FALSE--------------------------------------------------------------
# output <- LDA(TrainData, TrainCat, TestData,
# Method = "Projected", Mode = "Interactive")$Predictions
# "Please enter the number m1 of group 1 compression samples: "700
# "Please enter the number m2 of group 2 compression samples: "300
# "Please enter sparsity level s used in compression: "0.01
#
## -----------------------------------------------------------------------------
test_pred <- LDA(TrainData, TrainCat, TestData,
Method = "Projected", Mode = "Research",
m1 = 700, m2 = 300, s = 0.01)$Predictions
table(test_pred)
mean(output != TestCat)
## -----------------------------------------------------------------------------
test_pred <- LDA(TrainData, TrainCat, TestData,
Method = "fastRandomFisher", Mode = "Automatic")$Predictions
table(test_pred)
mean(test_pred != TestCat)
## -----------------------------------------------------------------------------
test_pred <- LDA(TrainData, TrainCat, TestData,
Method = "fastRandomFisher")$Predictions
table(test_pred)
mean(test_pred != TestCat)
## ---- eval=FALSE--------------------------------------------------------------
# output <- LDA(TrainData, TrainCat, TestData,
# Method = "fastRandomFisher", Mode = "Interactive")$Predictions
# "Please enter the number m of total compressed samples: "1000
# "Please enter sparsity level s used in compression: "0.01
## -----------------------------------------------------------------------------
test_pred <- LDA(TrainData, TrainCat, TestData,
Method = "fastRandomFisher", Mode = "Research",
m = 1000, s = 0.01)$Predictions
table(test_pred)
mean(test_pred != TestCat)
## -----------------------------------------------------------------------------
TrainData <- QDA_Data$TrainData
TrainCat <- QDA_Data$TrainCat
TestData <- QDA_Data$TestData
TestCat <- QDA_Data$TestCat
## -----------------------------------------------------------------------------
Predictions <- QDA(TrainData, TrainCat, TestData, Method = "Full")
table(Predictions)
## -----------------------------------------------------------------------------
output <- QDA(TrainData, TrainCat, TestData, Method = "Compressed", Mode = "Automatic")
table(output)
## -----------------------------------------------------------------------------
output <- QDA(TrainData, TrainCat, TestData, Method = "Compressed")
table(output)
## ---- eval=FALSE--------------------------------------------------------------
# output <- QDA(TrainData, TrainCat, TestData, Method = "Compressed", Mode = "Interactive")
# "Please enter the number m1 of group 1 compression samples: "700
# "Please enter the number m2 of group 2 compression samples: "300
# "Please enter sparsity level s used in compression: "0.01
#
# table(output)
## -----------------------------------------------------------------------------
output <- QDA(TrainData, TrainCat, TestData, Method = "Compressed",
Mode = "Research", m1 = 700, m2 = 300, s = 0.01)
summary(output)
## -----------------------------------------------------------------------------
output <- QDA(TrainData, TrainCat, TestData, Method = "Subsampled", Mode = "Automatic")
table(output)
## -----------------------------------------------------------------------------
output <- QDA(TrainData, TrainCat, TestData, Method = "Subsampled")
summary(output)
## ---- eval=FALSE--------------------------------------------------------------
# output <- QDA(TrainData, TrainCat, TestData, Method = "Subsampled", Mode = "Interactive")
# "Please enter the number m1 of group 1 sub-samples: "700
# "Please enter the number m2 of group 2 sub-samples: "300
#
# summary(output)
## -----------------------------------------------------------------------------
output <- QDA(TrainData, TrainCat, TestData, Method = "Subsampled",
Mode = "Research", m1 = 700, m2 = 300)
summary(output)
## -----------------------------------------------------------------------------
TrainData <- KOS_Data$TrainData
TrainCat <- KOS_Data$TrainCat
TestData <- KOS_Data$TestData
TestCat <- KOS_Data$TestCat
## ---- eval = FALSE------------------------------------------------------------
# > SelectParams(TrainData, TrainCat)
#
# $Sigma
# [1] 0.7390306
#
# $Gamma
# [1] 0.137591
#
# $Lambda
# [1] 0.0401767
## ---- eval = FALSE------------------------------------------------------------
# > SelectParams(TrainData, TrainCat, Sigma = 1, Gamma = 0.1)
#
# $Sigma
# [1] 1
#
# $Gamma
# [1] 0.1
#
# $Lambda
# [1] 0.06186337
## ---- eval = FALSE------------------------------------------------------------
# SelectParams(TrainData, TrainCat, Gamma = 0.1)
#
# Error in SelectParams(TrainData, TrainCat, Gamma = 0.1) :
# Hierarchical order of parameters violated.
# Please specify Sigma before Gamma, and both Sigma and Gamma before Lambda.
## ---- eval = FALSE------------------------------------------------------------
# Sigma <- 1.325386
# Gamma <- 0.07531579
# Lambda <- 0.002855275
#
# > output <- KOS(TestData, TrainData, TrainCat, Sigma = Sigma,
# Gamma = Gamma, Lambda = Lambda)
#
# > output$Weight
# [1] 1 1 0 0
#
# > table(output$Predictions)
# 1 2
# 26 68
#
# > summary(output$Dvec)
# V1
# Min. :-0.05860
# 1st Qu.:-0.03711
# Median :-0.02539
# Mean : 0.00000
# 3rd Qu.: 0.06983
# Max. : 0.10192
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.