## Not run:
# DB <- RODM_open_dbms_connection(dsn="orcl11g", uid= "rodm", pwd = "rodm")
#
# ### Classification
#
# # Predicting survival in the sinking of the Titanic based on pasenger's sex, age, class, etc.
#
#
# data(titanic3, package="PASWR") # Load survival data from Titanic
# ds <- titanic3[,c("pclass", "survived", "sex", "age", "fare", "embarked")] # Select subset of attributes
# ds[,"survived"] <- ifelse(ds[,"survived"] == 1, "Yes", "No") # Rename target values
# n.rows <- length(ds[,1]) # Number of rows
# random_sample <- sample(1:n.rows, ceiling(n.rows/2)) # Split dataset randomly in train/test subsets
# titanic_train <- ds[random_sample,] # Training set
# titanic_test <- ds[setdiff(1:n.rows, random_sample),] # Test set
# RODM_create_dbms_table(DB, "titanic_train") # Push the training table to the database
# RODM_create_dbms_table(DB, "titanic_test") # Push the testing table to the database
# svm <- RODM_create_svm_model(database = DB, # Create ODM SVM classification model
# data_table_name = "titanic_train",
# target_column_name = "survived",
# model_name = "SVM_MODEL",
# mining_function = "classification")
#
# # Apply the SVM classification model to test data.
# svm2 <- RODM_apply_model(database = DB, # Predict test data
# data_table_name = "titanic_test",
# model_name = "SVM_MODEL",
# supplemental_cols = "survived")
#
# print(svm2$model.apply.results[1:10,]) # Print example of prediction results
# actual <- svm2$model.apply.results[, "SURVIVED"]
# predicted <- svm2$model.apply.results[, "PREDICTION"]
# probs <- as.real(as.character(svm2$model.apply.results[, "'Yes'"]))
# table(actual, predicted, dnn = c("Actual", "Predicted")) # Confusion matrix
# library(verification)
# perf.auc <- roc.area(ifelse(actual == "Yes", 1, 0), probs) # Compute ROC and plot
# auc.roc <- signif(perf.auc$A, digits=3)
# auc.roc.p <- signif(perf.auc$p.value, digits=3)
# roc.plot(ifelse(actual == "Yes", 1, 0), probs, binormal=T, plot="both", xlab="False Positive Rate",
# ylab="True Postive Rate", main= "Titanic survival ODM SVM model ROC Curve")
# text(0.7, 0.4, labels= paste("AUC ROC:", signif(perf.auc$A, digits=3)))
# text(0.7, 0.3, labels= paste("p-value:", signif(perf.auc$p.value, digits=3)))
#
# RODM_drop_model(DB, "SVM_MODEL") # Drop the model
# RODM_drop_dbms_table(DB, "titanic_train") # Drop the training table in the database
# RODM_drop_dbms_table(DB, "titanic_test") # Drop the testing table in the database
# ## End(Not run)
### Regression
# Aproximating a one-dimensional non-linear function
## Not run:
# X1 <- 10 * runif(500) - 5
# Y1 <- X1*cos(X1) + 2*runif(500)
# ds <- data.frame(cbind(X1, Y1))
# RODM_create_dbms_table(DB, "ds") # Push the table to the database
# svm <- RODM_create_svm_model(database = DB, # Create ODM SVM regression model
# data_table_name = "ds",
# target_column_name = "Y1",
# model_name = "SVM_MODEL",
# mining_function = "regression")
#
# # Apply the SVM regression model to test data.
# svm2 <- RODM_apply_model(database = DB, # Predict training data
# data_table_name = "ds",
# model_name = "SVM_MODEL",
# supplemental_cols = "X1")
#
# plot(X1, Y1, pch=20, col="blue")
# points(x=svm2$model.apply.results[, "X1"], svm2$model.apply.results[, "PREDICTION"], pch=20, col="red")
# legend(-4, -1.5, legend = c("actual", "SVM regression"), pch = c(20, 20), col = c("blue", "red"),
# pt.bg = c("blue", "red"), cex = 1.20, pt.cex=1.5, bty="n")
#
# RODM_drop_model(DB, "SVM_MODEL") # Drop the model
# RODM_drop_dbms_table(DB, "ds") # Drop the database table
# ## End(Not run)
### Anomaly detection
# Finding outliers in a 2D-dimensional discrete distribution of points
## Not run:
# X1 <- c(rnorm(200, mean = 2, sd = 1), rnorm(300, mean = 8, sd = 2))
# Y1 <- c(rnorm(200, mean = 2, sd = 1.5), rnorm(300, mean = 8, sd = 1.5))
# ds <- data.frame(cbind(X1, Y1))
# RODM_create_dbms_table(DB, "ds") # Push the table to the database
# svm <- RODM_create_svm_model(database = DB, # Create ODM SVM anomaly detection model
# data_table_name = "ds",
# target_column_name = NULL,
# model_name = "SVM_MODEL",
# mining_function = "anomaly_detection")
#
# # Apply the SVM anomaly detection model to data.
# svm2 <- RODM_apply_model(database = DB, # Predict training data
# data_table_name = "ds",
# model_name = "SVM_MODEL",
# supplemental_cols = c("X1","Y1"))
#
# plot(X1, Y1, pch=20, col="white")
# col <- ifelse(svm2$model.apply.results[, "PREDICTION"] == 1, "green", "red")
# for (i in 1:500) points(x=svm2$model.apply.results[i, "X1"],
# y=svm2$model.apply.results[i, "Y1"],
# col = col[i], pch=20)
# legend(8, 2, legend = c("typical", "anomaly"), pch = c(20, 20), col = c("green", "red"),
# pt.bg = c("green", "red"), cex = 1.20, pt.cex=1.5, bty="n")
#
# RODM_drop_model(DB, "SVM_MODEL") # Drop the model
# RODM_drop_dbms_table(DB, "ds") # Drop the database table
# ## End(Not run)
### Clustering
# Clustering a 2D multi-Gaussian distribution of points into clusters
## Not run:
# set.seed(seed=6218945)
# X1 <- c(rnorm(100, mean = 2, sd = 1), rnorm(100, mean = 8, sd = 2), rnorm(100, mean = 5, sd = 0.6),
# rnorm(100, mean = 4, sd = 1), rnorm(100, mean = 10, sd = 1)) # Create and merge 5 Gaussian distributions
# Y1 <- c(rnorm(100, mean = 1, sd = 2), rnorm(100, mean = 4, sd = 1.5), rnorm(100, mean = 6, sd = 0.5),
# rnorm(100, mean = 3, sd = 0.2), rnorm(100, mean = 2, sd = 1))
# ds <- data.frame(cbind(X1, Y1))
# n.rows <- length(ds[,1]) # Number of rows
# row.id <- matrix(seq(1, n.rows), nrow=n.rows, ncol=1, dimnames= list(NULL, c("ROW_ID"))) # Row id
# ds <- cbind(row.id, ds) # Add row id to dataset
# RODM_create_dbms_table(DB, "ds")
# km <- RODM_create_kmeans_model(
# database = DB, # database ODBC channel identifier
# data_table_name = "ds", # data frame containing the input dataset
# case_id_column_name = "ROW_ID", # case id to enable assignments during build
# num_clusters = 5)
#
# # Apply the K-Means clustering model to data.
# km2 <- RODM_apply_model(
# database = DB, # database ODBC channel identifier
# data_table_name = "ds", # data frame containing the input dataset
# model_name = "KM_MODEL",
# supplemental_cols = c("X1","Y1"))
#
# x1a <- km2$model.apply.results[, "X1"]
# y1a <- km2$model.apply.results[, "Y1"]
# clu <- km2$model.apply.results[, "CLUSTER_ID"]
# c.numbers <- unique(as.numeric(clu))
# c.assign <- match(clu, c.numbers)
# color.map <- c("blue", "green", "red", "orange", "purple")
# color <- color.map[c.assign]
# nf <- layout(matrix(c(1, 2), 1, 2, byrow=T), widths = c(1, 1), heights = 1, respect = FALSE)
# plot(x1a, y1a, pch=20, col=1, xlab="X1", ylab="Y1", main="Original Data Points")
# plot(x1a, y1a, pch=20, type = "n", xlab="X1", ylab="Y1", main="After kmeans clustering")
# for (i in 1:n.rows) {
# points(x1a[i], y1a[i], col= color[i], pch=20)
# }
# legend(5, -0.5, legend=c("Cluster 1", "Cluster 2", "Cluster 3", "Cluster 4", "Cluster 5"), pch = rep(20, 5),
# col = color.map, pt.bg = color.map, cex = 0.8, pt.cex=1, bty="n")
#
# RODM_drop_model(DB, "KM_MODEL") # Drop the model
# RODM_drop_dbms_table(DB, "ds") # Drop the database table
#
#
# RODM_close_dbms_connection(DB)
# ## End(Not run)
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