classifier

package classifier

The analytics package contains classes, traits and objects for analytics focused on classification.

Linear Supertypes

AnyRef, Any

Ordering

Alphabetic
By Inheritance

Inherited

classifier
AnyRef
Any

Hide All
Show All

Visibility

Public
All

Type Members

abstract class BayesClassifier extends ClassifierInt with BayesMetrics
The BayesClassifier abstract class provides base methods for building Bayesian Classifiers.
The BayesClassifier abstract class provides base methods for building Bayesian Classifiers. The following types of classifiers are currently supported: NaiveBayes - Naive Bayes classifier OneBAN - Augmented Naive Bayes (1-BAN) classifier TANBayes - Tree Augmented Naive Bayes classifier TwoBAN_OS - Ordering-based Bayesian Network (2-BAN with Order Swapping) -----------------------------------------------------------------------------
class BayesClfML extends Classifier
The BayesClfML class implements an Integer-Based Naive Bayes Multi-Label Classifier, which is a commonly used such classifier for discrete input data.
The BayesClfML class implements an Integer-Based Naive Bayes Multi-Label Classifier, which is a commonly used such classifier for discrete input data. The classifier is trained using a data matrix 'x' and a classification matrix 'y'. Each data vector in the matrix is classified into one of 'k' classes numbered 0, ..., k-1. Prior probabilities are calculated based on the population of each class in the training-set. Relative posterior probabilities are computed by multiplying these by values computed using conditional probabilities. The classifier is naive, because it assumes feature independence and therefore simply multiplies the conditional probabilities.

See also
www.aia-i.com/ijai/sample/vol3/no2/173-188.pdf -----------------------------------------------------------------------------
trait BayesMetrics extends AnyRef
The BayesMetrics trait provides scoring methods.
class BayesNetwork extends BayesClassifier
The BayesNetwork class implements a Bayesian Network Classifier.
The BayesNetwork class implements a Bayesian Network Classifier. It classifies a data vector 'z' by determining which of 'k' classes has the highest Joint Probability of 'z' and the outcome (i.e., one of the 'k' classes) of occurring. The Joint Probability calculation is factored into multiple calculations of Conditional Probability. Conditional dependencies are specified using a Directed Acyclic Graph 'DAG'. Nodes are conditionally dependent on their parents only. Conditional probability are recorded in tables. Training is achieved by ... -----------------------------------------------------------------------------
trait Classifier extends Model
The Classifier trait provides a common framework for several classifiers.
The Classifier trait provides a common framework for several classifiers. A classifier is for bounded responses. When the number of distinct responses cannot be bounded by some integer 'k', a predictor should be used.
abstract class ClassifierInt extends ConfusionFit with Classifier
The ClassifierInt abstract class provides a common foundation for several classifiers that operate on integer-valued data.
abstract class ClassifierReal extends ConfusionFit with Classifier
The ClassifierReal abstract class provides a common foundation for several classifiers that operate on real-valued data.
The ClassifierReal abstract class provides a common foundation for several classifiers that operate on real-valued data. Classes: DecisionTreeC45, KNN_Classifier, LDA.scala, LogisticRegression, NaiveBayesR, RandomForest, RandomForest2, SimpleLDA, SimpleLogisticRegression, SupportVectorMachine.
class ConfusionFit extends QoF with Error
The ConfusionFit class provides functions for determining the confusion matrix as well as derived Quality of Fit (QoF) measures such as pseudo R-squared, sst, sse, accuracy, precsion, recall, specificity and Cohen's kappa coefficient.
The ConfusionFit class provides functions for determining the confusion matrix as well as derived Quality of Fit (QoF) measures such as pseudo R-squared, sst, sse, accuracy, precsion, recall, specificity and Cohen's kappa coefficient.

See also
analytics.Fit ------------------------------------------------------------------------------ Must call the 'confusion' method before calling the other methods. ------------------------------------------------------------------------------
class ConvNetLayer_1D extends AnyRef
The ConvNetLayer_1D class implements a Convolutionsl Net Layer, meant to be part of Convolutionsl Net classifier.
The ConvNetLayer_1D class implements a Convolutionsl Net Layer, meant to be part of Convolutionsl Net classifier. The classifier is trained using a data tensor 'x' and a classification vector 'y'.
class ConvNetLayer_2D extends AnyRef
The ConvNetLayer_2D class implements a Convolutionsl Net Layer, meant to be part of Convolutionsl Net classifier.
The ConvNetLayer_2D class implements a Convolutionsl Net Layer, meant to be part of Convolutionsl Net classifier. The classifier is trained using a data tensor 'x' and a classification vector 'y'. Each row in the tensor is classified into one of 'k' classes numbered '0, ...0, k-1'. Each column, depth in the tensor represents a 2D structure (e.g., an image).
class DAG extends AnyRef
The 'DAG' class provides a data structure for storing directed acyclic graphs.
trait DecisionTree extends Error
The DecisionTree trait provides common capabilities for all types of decision trees.
class DecisionTreeC45 extends ClassifierReal with DecisionTree
The DecisionTreeC45 class implements a Decision Tree classifier using the C45 algorithm.
The DecisionTreeC45 class implements a Decision Tree classifier using the C45 algorithm. The classifier is trained using a data matrix 'x' and a classification vector 'y'. Each data vector in the matrix is classified into one of 'k' classes numbered '0, ..., k-1'. Each column in the matrix represents a feature (e.g., Humidity).
class DecisionTreeC45wp extends DecisionTreeC45
The DecisionTreeC45wp class extends DecisionTreeC45 with pruning capabilities.
The DecisionTreeC45wp class extends DecisionTreeC45 with pruning capabilities. The base class uses the C45 algorithm to construct a decision tree for classifying instance vectors.
class DecisionTreeID3 extends ClassifierInt with DecisionTree
The DecisionTreeID3 class implements a Decision Tree classifier using the ID3 algorithm.
The DecisionTreeID3 class implements a Decision Tree classifier using the ID3 algorithm. The classifier is trained using a data matrix 'x' and a classification vector 'y'. Each data vector in the matrix is classified into one of 'k' classes numbered '0, ..., k-1'. Each column in the matrix represents a feature (e.g., Humidity).
class DecisionTreeID3wp extends DecisionTreeID3
The DecisionTreeID3wp class extends DecisionTreeID3 with pruning capabilities.
The DecisionTreeID3wp class extends DecisionTreeID3 with pruning capabilities. The base class uses the ID3 algorithm to construct a decision tree for classifying instance vectors.
class FANBayes extends FANBayes0
The same classifier but uses an optimized cross-validation technique.
The same classifier but uses an optimized cross-validation technique. -----------------------------------------------------------------------------
class FANBayes0 extends BayesClassifier
The FANBayes0 class implements an Integer-Based Tree Augmented Naive Bayes Classifier, which is a commonly used such classifier for discrete input data.
The FANBayes0 class implements an Integer-Based Tree Augmented Naive Bayes Classifier, which is a commonly used such classifier for discrete input data. The classifier is trained using a data matrix 'x' and a classification vector 'y'. Each data vector in the matrix is classified into one of 'k' classes numbered 0, ..., k-1. Prior probabilities are calculated based on the population of each class in the training-set. Relative posterior probabilities are computed by multiplying these by values computed using conditional probabilities. The classifier supports limited dependency between features/variables. ----------------------------------------------------------------------------- This classifier uses the standard cross-validation technique. -----------------------------------------------------------------------------
class Filter extends AnyRef
The Filter class holds a filter for averaging a region of an input matrix.
class Filter1D extends AnyRef
The Filter class holds a filter for averaging a region of an input vector.
class GMM extends Classifier
The GMM class is used for univariate Gaussian Mixture Models.
The GMM class is used for univariate Gaussian Mixture Models. Given a sample, thought to be generated according to 'k' Normal distributions, estimate the values for the 'mu' and 'sig2' parameters for the Normal distributions. Given a new value, determine which class (0, ..., k-1) it is most likely to have come from. FIX: need a class for multivariate Gaussian Mixture Models. FIX: need to adapt for clustering. -----------------------------------------------------------------------------
class HiddenMarkov extends ClassifierInt
The HiddenMarkov classes provides Hidden Markov Models (HMM).
The HiddenMarkov classes provides Hidden Markov Models (HMM). An HMM model consists of a probability vector 'pi' and probability matrices 'a' and 'b'. The discrete-time system is characterized by a hidden state 'x(t)' and an observed symbol/value 'y(t)' at time 't', which may be viewed as a time series.
pi(i) = P(x(t) = i) a(i, j) = P(x(t+1) = j | x(t) = i) b(i, k) = P(y(t) = k | x(t) = i)
model (pi, a, b)
class KNN_Classifier extends ClassifierReal
The KNN_Classifier class is used to classify a new vector 'z' into one of 'k' classes.
The KNN_Classifier class is used to classify a new vector 'z' into one of 'k' classes. It works by finding its 'kappa' nearest neighbors. These neighbors essentially vote according to their classification. The class with most votes is selected as the classification of 'z'. Using a distance metric, the 'kappa' vectors nearest to 'z' are found in the training data, which is stored row-wise in the data matrix 'x'. The corresponding classifications are given in the vector 'y', such that the classification for vector 'x(i)' is given by 'y(i)'. FIX - cross validation uses test data for decision making, so when k = 1, acc = 100%
class LDA extends ClassifierReal
The LDA class implements a Linear Discriminant Analysis 'LDA' classifier.
The LDA class implements a Linear Discriminant Analysis 'LDA' classifier. It places a vector into a group according to its maximal discriminant function. FIX - currently only works when the number of classes 'k' = 2.

See also
en.wikipedia.org/wiki/Linear_discriminant_analysis
class LogisticRegression extends ClassifierReal
The LogisticRegression class supports (binomial) logistic regression.
The LogisticRegression class supports (binomial) logistic regression. In this case, 'x' may be multi-dimensional '[1, x_1, ... x_k]'. Fit the parameter vector 'b' in the logistic regression equation
logit (p_y) = b dot x + e = b_0 + b_1 * x_1 + ... b_k * x_k + e
where 'e' represents the residuals (the part not explained by the model) and 'y' is now binary.

See also
see.stanford.edu/materials/lsoeldsee263/05-ls.pdf
class NaiveBayes extends NaiveBayes0
The same classifier but uses an optimized cross-validation technique.
The same classifier but uses an optimized cross-validation technique. -----------------------------------------------------------------------------
class NaiveBayes0 extends BayesClassifier
The NaiveBayes0 class implements an Integer-Based Naive Bayes Classifier, which is a commonly used such classifier for discrete input data.
The NaiveBayes0 class implements an Integer-Based Naive Bayes Classifier, which is a commonly used such classifier for discrete input data. The classifier is trained using a data matrix 'x' and a classification vector 'y'. Each data vector in the matrix is classified into one of 'k' classes numbered 0, ..., k-1. Prior probabilities are calculated based on the population of each class in the training-set. Relative posterior probabilities are computed by multiplying these by values computed using conditional probabilities. The classifier is naive, because it assumes feature independence and therefore simply multiplies the conditional probabilities. ----------------------------------------------------------------------------- This classifier uses the standard cross-validation technique. -----------------------------------------------------------------------------
class NaiveBayesR extends ClassifierReal
The NaiveBayesR class implements a Gaussian Naive Bayes Classifier, which is the most commonly used such classifier for continuous input data.
The NaiveBayesR class implements a Gaussian Naive Bayes Classifier, which is the most commonly used such classifier for continuous input data. The classifier is trained using a data matrix 'x' and a classification vector 'y'. Each data vector in the matrix is classified into one of 'k' classes numbered 0, ..., k-1. Class probabilities are calculated based on the frequency of each class in the training-set. Relative probabilities are computed by multiplying these by values computed using conditional density functions based on the Normal (Gaussian) distribution. The classifier is naive, because it assumes feature independence and therefore simply multiplies the conditional densities. -----------------------------------------------------------------------------
class NeuralNet_Classif_3L extends NeuralNet_3L
The NeuralNet_Classif_3L class supports multi-output, 3-layer (input, hidden and output) Neural-Network classifiers.
The NeuralNet_Classif_3L class supports multi-output, 3-layer (input, hidden and output) Neural-Network classifiers. Given several input vectors and output vectors (training data), fit the parameters 'a' and 'b' connecting the layers, so that for a new input vector 'v', the net can classify the output value, i.e.,
yp = f1 (b * f0 (a * v))
where 'f0' and 'f1' are the activation functions and the parameter 'a' and 'b' are the parameters between input-hidden and hidden-output layers. Note: 'f1' is set to 'f_sigmoid'
class NeuralNet_Classif_XL extends NeuralNet_XL
The NeuralNet_Classif_XL class supports multi-output, multi-layer (input, {hidden} and output) Neural-Network classifiers.
The NeuralNet_Classif_XL class supports multi-output, multi-layer (input, {hidden} and output) Neural-Network classifiers. Given several input vectors and output vectors (training data), fit the parameters connecting the layers, so that for a new input vector 'v', the net can classify the output value. Note: 'f.last' is set to 'f_sigmoid'
case class Node(f: Int, gn: Double, nu: VectoI, parent: Node = null, y: Int, isLeaf: Boolean = false) extends Cloneable with Product with Serializable
The Node class is used to hold information about a node in the decision tree.
The Node class is used to hold information about a node in the decision tree.
f
the feature/variable number used for splitting (negative => leaf)
gn
the information gain recorded at this node
nu
the frequency count
parent
the parent node (null for root)
y
the response/decision value
isLeaf
whether the node is a leaf (terminal node)
class NullModel extends ClassifierInt
The NullModel class implements a Null Model Classifier, which is a simple classifier for discrete input data.
The NullModel class implements a Null Model Classifier, which is a simple classifier for discrete input data. The classifier is trained just using a classification vector 'y'. Picks the most frequent class. Each data instance is classified into one of 'k' classes numbered 0, ..., k-1. Note: also works for ClassifierReal problems.
class OneBAN extends OneBAN0
The same classifier but uses an optimized cross-validation technique.
The same classifier but uses an optimized cross-validation technique. -----------------------------------------------------------------------------
class OneBAN0 extends BayesClassifier
The OneBAN class implements an Integer-Based One-parent BN Augmented Naive Bayes Classifier, which is a commonly used such classifier for discrete input data.
The OneBAN class implements an Integer-Based One-parent BN Augmented Naive Bayes Classifier, which is a commonly used such classifier for discrete input data. The classifier is trained using a data matrix 'x' and a classification vector 'y'. Each data vector in the matrix is classified into one of 'k' classes numbered 0, ..., k-1. Prior probabilities are calculated based on the population of each class in the training-set. Relative posterior probabilities are computed by multiplying these by values computed using conditional probabilities. The classifier supports limited dependency between features/variables. ----------------------------------------------------------------------------- This classifier uses the standard cross-validation technique. -----------------------------------------------------------------------------
class PGMHD3 extends BayesClassifier
The PGMHD3 class implements a three-level Bayes Classifier for discrete input data.
The PGMHD3 class implements a three-level Bayes Classifier for discrete input data. The classifier is trained using a data matrix 'x' and a classification vector 'y'. Each data vector in the matrix is classified into one of 'k' classes numbered 0, ..., k-1. Prior probabilities are calculated based on the population of each class in the training-set. Relative posterior probabilities are computed by multiplying these by values computed using conditional probabilities. The classifier is naive, because it assumes feature independence and therefore simply multiplies the conditional probabilities. ----------------------------------------------------------------------------- [ x ] -> [ x z ] where x features are level 2 and z features are level 3. -----------------------------------------------------------------------------
class PGMHD3cp extends BayesClassifier
The PGMHD3cp class implements a three-level Bayes Classifier for discrete input data.
The PGMHD3cp class implements a three-level Bayes Classifier for discrete input data. The classifier is trained using a data matrix 'x' and a classification vector 'y'. Each data vector in the matrix is classified into one of 'k' classes numbered 0, ..., k-1. Prior probabilities are calculated based on the frequency/population of each class in the training-set. Relative posterior probabilities are computed by multiplying these by values computed using conditional probabilities. The classifier is naive, because it assumes feature independence and therefore simply multiplies the conditional probabilities. ----------------------------------------------------------------------------- [ x ] -> [ x z ] where x features are level 2 and z features are level 3. -----------------------------------------------------------------------------
class PGMHD3fl extends BayesClassifier
The PGMHD3fl class implements a three-level Probabilistic Classifier for discrete (binary) input data, based on flow from bottom to top levels.
The PGMHD3fl class implements a three-level Probabilistic Classifier for discrete (binary) input data, based on flow from bottom to top levels. The classifier is trained using the following data matrices:
'x' - the mid/features 'X' (level 1) 'z' - the low/feature 'Z' (level 2) 'y' - the top/class 'C' (level 0)
Each random variable 'C_j, X_k and Z_l' is binary ('vc = 2', 'k = 2'), where '1' indicates occurrence, while '0' indicates no evidence of occurreence. Frequency counts and classification scores are computed from a training-set. -----------------------------------------------------------------------------
class RandomForest extends ClassifierReal
The RandomForest class uses several randomly built descision trees for classification.
The RandomForest class uses several randomly built descision trees for classification. It randomly selects sub-samples of 'bRatio * x.dim1' size from the data 'x' and 'y' to build 'nTrees' decision trees. The 'classify' method uses voting from all of the trees. Note: this version does not select sub-features to build the trees.
class RandomForest2 extends RandomForest
The RandomForest2 class uses randomness for building descision trees in classification.
The RandomForest2 class uses randomness for building descision trees in classification. It randomly selects sub-samples with 'size = bRatio * sample-size' from the sample (with replacement) and uses the 'fbRatio' fraction of sub-features to build the trees, and to classify by voting from all of the trees.
class SimpleLDA extends ClassifierReal
The SimpleLDA class implements a Linear Discriminant Analysis 'LDA' classifier.
The SimpleLDA class implements a Linear Discriminant Analysis 'LDA' classifier. It places a value into a group according to its maximal discriminant function.

See also
en.wikipedia.org/wiki/Linear_discriminant_analysis
class SimpleLogisticRegression extends ClassifierReal
The SimpleLogisticRegression class supports (binomial) logistic regression.
The SimpleLogisticRegression class supports (binomial) logistic regression. In this case, 'x' is two-dimensional '[1, x_1]'. Fit the parameter vector 'b' in the logistic regression equation
logit (p_y) = b dot x + e = b_0 + b_1 * x_1 + e
where 'e' represents the residuals (the part not explained by the model) and 'y' is now binary.

See also
see.stanford.edu/materials/lsoeldsee263/05-ls.pdf
type Strings = Array[String]
Shorthand for array of strings
class SupportVectorMachine extends ClassifierReal
The SupportVectorMachine class is a translation of Pseudo-Code from a modified SMO (Modification 2) found at the above URL's into Scala and includes a few simplifications (e.g., currently only works for linear kernels, dense data and binary classification).
class TANBayes extends TANBayes0
The same classifier but uses an optimized cross-validation technique.
The same classifier but uses an optimized cross-validation technique. -----------------------------------------------------------------------------
class TANBayes0 extends BayesClassifier
The TANBayes0 class implements an Integer-Based Tree Augmented Naive Bayes Classifier, which is a commonly used such classifier for discrete input data.
The TANBayes0 class implements an Integer-Based Tree Augmented Naive Bayes Classifier, which is a commonly used such classifier for discrete input data. The classifier is trained using a data matrix 'x' and a classification vector 'y'. Each data vector in the matrix is classified into one of 'k' classes numbered 0, ..., k-1. Prior probabilities are calculated based on the population of each class in the training-set. Relative posterior probabilities are computed by multiplying these by values computed using conditional probabilities. The classifier supports limited dependency between features/variables. ----------------------------------------------------------------------------- This classifier uses the standard cross-validation technique. -----------------------------------------------------------------------------
class TabuFeatures extends AnyRef
The TabuFeatures keeps track of pairs of features, so they are not repeatedly tried.
The TabuFeatures keeps track of pairs of features, so they are not repeatedly tried.

See also
TwoBAN_OS
class TwoBAN_OS extends TwoBAN_OS0
The same classifier but uses an optimized cross-validation technique ------------------------------------------------------------------------------
class TwoBAN_OS0 extends BayesClassifier
The TwoBAN_OS0 class implements an Integer-Based Bayesian Network Classifier, which is a commonly used such classifier for discrete input data.
The TwoBAN_OS0 class implements an Integer-Based Bayesian Network Classifier, which is a commonly used such classifier for discrete input data. Each node is limited to have at most 2 parents, and hence the "2" in the class name TwoBAN_OS0. The classifier is trained using a data matrix 'x' and a classification vector 'y'. Each data vector in the matrix is classified into one of 'k' classes numbered 0, ..., k-1. Prior probabilities are calculated based on the population of each class in the training-set. Relative posterior probabilities are computed by multiplying these by values computed using conditional probabilities. The classifier supports limited dependency between features/variables. ------------------------------------------------------------------------------ This classifier uses the standard cross-validation technique. ------------------------------------------------------------------------------

Value Members

val BASE_DIR: String
The relative path for base directory
def findSplit(xj: VectoD, y: VectoI, idx_: IndexedSeq[Int] = null, k: Int = 2): Double
Find the best split threshold 'thres' that divides feature/variable 'xj' into low (<= 'thesh') and high (> 'thres') values such that weighted entropy is minimized.
Find the best split threshold 'thres' that divides feature/variable 'xj' into low (<= 'thesh') and high (> 'thres') values such that weighted entropy is minimized.
xj
the vector for feature fea (column j of matrix)
y
the classification/response vector
idx_
the index positions within x (if null, use all index positions)
k
the number of classes
def range2muSet(r: Range): Set[Int]
Convert a Range into a mutable.Set containing all the elements in the range.
Convert a Range into a mutable.Set containing all the elements in the range.
r
the range to be converted
object BayesClassifier
The BayesClassifier object provides factory methods for building Bayesian Classifiers.
object BayesClassifierTest extends App
The BayesClassifierTest object is used to test the BayesClassifier class.
The BayesClassifierTest object is used to test the BayesClassifier class. Classify whether a car is more likely to be stolen (1) or not (1).

See also
www.inf.u-szeged.hu/~ormandi/ai2/06-naiveBayes-example.pdf > runMain scalation.analytics.classifier.BayesClassifierTest
object BayesClassifierTest10 extends App
The BayesClassifierTest10 object is used to test the BayesClassifier class.
The BayesClassifierTest10 object is used to test the BayesClassifier class. > runMain scalation.analytics.classifier.BayesClassifierTest10
object BayesClassifierTest11 extends App
The BayesClassifierTest11 object is used to test the BayesClassifier class.
The BayesClassifierTest11 object is used to test the BayesClassifier class. > runMain scalation.analytics.classifier.BayesClassifierTest11
object BayesClassifierTest2 extends App
The BayesClassifierTest2 object is used to test the BayesClassifier class.
The BayesClassifierTest2 object is used to test the BayesClassifier class. > runMain scalation.analytics.classifier.BayesClassifierTest2
object BayesClassifierTest3 extends App
The BayesClassifierTest3 object is used to test the BayesClassifier class.
The BayesClassifierTest3 object is used to test the BayesClassifier class. > runMain scalation.analytics.classifier.BayesClassifierTest3
object BayesClassifierTest4 extends App
The BayesClassifierTest4 object is used to test the BayesClassifier class.
The BayesClassifierTest4 object is used to test the BayesClassifier class. > runMain scalation.analytics.classifier.BayesClassifierTest4
object BayesClassifierTest5 extends App
The BayesClassifierTest5 object is used to test the BayesClassifier class.
The BayesClassifierTest5 object is used to test the BayesClassifier class. > runMain scalation.analytics.classifier.BayesClassifierTest5
object BayesClassifierTest6 extends App
The BayesClassifierTest6 object is used to test the BayesClassifier class.
The BayesClassifierTest6 object is used to test the BayesClassifier class. > runMain scalation.analytics.classifier.BayesClassifierTest6
object BayesClassifierTest7 extends App
The BayesClassifierTest7 object is used to test the BayesClassifier class.
The BayesClassifierTest7 object is used to test the BayesClassifier class. > runMain scalation.analytics.classifier.BayesClassifierTest7
object BayesClassifierTest8 extends App
The BayesClassifierTest8 object is used to test the BayesClassifier class.
The BayesClassifierTest8 object is used to test the BayesClassifier class. > runMain scalation.analytics.classifier.BayesClassifierTest8
object BayesClassifierTest9 extends App
The BayesClassifierTest9 object is used to test the BayesClassifier class.
The BayesClassifierTest9 object is used to test the BayesClassifier class. > runMain scalation.analytics.classifier.BayesClassifierTest9
object BayesClfMLTest extends App
The BayesClfMLTest object is used to test the BayesClfML class.
The BayesClfMLTest object is used to test the BayesClfML class. Classify whether a car is more likely to be stolen (1) or not (1).

See also
www.inf.u-szeged.hu/~ormandi/ai2/06-naiveBayes-example.pdf > runMain scalation.analytics.classifier.BayesClfMLTest
object BayesNetworkTest extends App
The BayesNetworkTest object is used to test the BayesNetwork class.
The BayesNetworkTest object is used to test the BayesNetwork class. Ex: Classify whether a person has a Back Ache.

See also
www.eng.tau.ac.il/~bengal/BN.pdf > runMain sclation.analytics.classifier.BayesNetworkTest
object Classifier
The Classifier object provides methods for paritioning the downsampling the the dataset.
object ClassifierInt
The ClassifierInt companion object provides methods to read in data matrices in a combined 'xy' format that can be later decomposed into 'x' the feature data matrix and 'y' the classification vector.
object ClassifierReal
The ClassifierReal object provides helper methods.
object ConfusionFit
The ConfusionFit companion object records the indicies and labels for the base Quality of Fit (QoF) measures for the classification techniques.
object ConfusionFitTest extends App
The ConfusionFitTest object is used to test the ConfusionFit class.
The ConfusionFitTest object is used to test the ConfusionFit class. > runMain scalation.analytics.classifier.ConfusionFitTest
object ConfusionFitTest2 extends App
The ConfusionFitTest2 object is used to test the ConfusionFit class.
The ConfusionFitTest2 object is used to test the ConfusionFit class.

See also
www.quora.com/How-do-I-compute-precision-and-recall-values-for-a-dataset > runMain scalation.analytics.classifier.ConfusionFitTest2
object ConfusionFitTest3 extends App
The ConfusionFitTest3 object is used to test the ConfusionFit class.
The ConfusionFitTest3 object is used to test the ConfusionFit class.

See also
towardsdatascience.com/multi-class-metrics-made-simple-part-i-precision-and-recall-9250280bddc2 Note: ScalaTion's confusion matrix is the transpose of the one on the Website > runMain scalation.analytics.classifier.ConfusionFitTest3
object ConvNetLayer_1D
The ConvNetLayer_1D compnanion object provides factory functions for the ConvNetLayer_1D class.
object ConvNetLayer_1DTest extends App
The ConvNetLayer_1DTest object is used to test the ConvNetLayer_1D class.
The ConvNetLayer_1DTest object is used to test the ConvNetLayer_1D class. Test using the simple example from section 11.10 of ScalaTion textbook. > runMain scalation.analytics.classifier.ConvNetLayer_1DTest
object ConvNetLayer_2D
The ConvNetLayer_2D compnanion object provides factory functions for the ConvNetLayer_2D class.
object ConvNetLayer_2DTest extends App
The ConvNetLayer_2DTest object is used to test the ConvNetLayer_2D class.
The ConvNetLayer_2DTest object is used to test the ConvNetLayer_2D class. Test using the simple example from section 11.11 of ScalaTion textbook. > runMain scalation.analytics.classifier.ConvNetLayer_2DTest
object ConvNetLayer_2DTest2 extends App
The ConvNetLayer_2DTest2 object is used to test the ConvNetLayer_2D class.
The ConvNetLayer_2DTest2 object is used to test the ConvNetLayer_2D class. Test the first two images in MNIST.

See also
http://rasbt.github.io/mlxtend/user_guide/data/mnist_data > runMain scalation.analytics.classifier.ConvNetLayer_2DTest2
object ConvNetLayer_2DTest3 extends App
The ConvNetLayer_2DTest3 object is used to test the ConvNetLayer_2D class.
The ConvNetLayer_2DTest3 object is used to test the ConvNetLayer_2D class. Test the MNIST dataset of 10,000 images. > runMain scalation.analytics.classifier.ConvNetLayer_2DTest3
object DecisionTree
The DecisionTree companion object provides the hyper-parameters for the decision trees and random forests.
The DecisionTree companion object provides the hyper-parameters for the decision trees and random forests.

See also
scalation.analytics.HyperParameter
object DecisionTreeC45
The DecisionTreeC45 companion object provides factory methods.
object DecisionTreeC45Test extends App
The DecisionTreeC45Test object is used to test the DecisionTreeC45 class.
The DecisionTreeC45Test object is used to test the DecisionTreeC45 class. Ex: Classify (No/Yes) whether a person will play tennis based on the measured features.

See also
www.cise.ufl.edu/~ddd/cap6635/Fall-97/Short-papers/2.htm > runMain scalation.analytics.classifier.DecisionTreeC45Test
object DecisionTreeC45Test2 extends App
The DecisionTreeC45Test2 object is used to test the DecisionTreeC45 class.
The DecisionTreeC45Test2 object is used to test the DecisionTreeC45 class. Ex: Classify (No/Yes) whether a person will play tennis based on the measured features.

See also
www.cise.ufl.edu/~ddd/cap6635/Fall-97/Short-papers/2.htm > runMain scalation.analytics.classifier.DecisionTreeC45Test2
object DecisionTreeC45Test3 extends App
The DecisionTreeC45Test3 object is used to test the DecisionTreeC45 class.
The DecisionTreeC45Test3 object is used to test the DecisionTreeC45 class. Ex: Classify whether a there is breast cancer. > runMain scalation.analytics.classifier.DecisionTreeC45Test3
object DecisionTreeC45Test4 extends App
The DecisionTreeC45Test4 object is used to test the DecisionTreeC45 class.
The DecisionTreeC45Test4 object is used to test the DecisionTreeC45 class. Ex: Classify the quality of white wine. > runMain scalation.analytics.classifier.DecisionTreeC45Test4
object DecisionTreeC45Test5 extends App
The DecisionTreeC45Test5 object is used to test the DecisionTreeC45 class.
The DecisionTreeC45Test5 object is used to test the DecisionTreeC45 class. Ex: Classify whether the patient has diabetes or not > runMain scalation.analytics.classifier.DecisionTreeC45Test5
object DecisionTreeC45wp extends App
The DecisionTreeC45wp companion object provides a factory function.
object DecisionTreeC45wpTest extends App
The DecisionTreeC45wpTest object is used to test the DecisionTreeC45wp class.
The DecisionTreeC45wpTest object is used to test the DecisionTreeC45wp class. > runMain scalation.analytics.classifier.DecisionTreeC45wpTest
object DecisionTreeC45wpTest2 extends App
The DecisionTreeC45wpTest2 object is used to test the DecisionTreeC45wp class.
The DecisionTreeC45wpTest2 object is used to test the DecisionTreeC45wp class. > runMain scalation.analytics.classifier.DecisionTreeC45wpTest2
object DecisionTreeID3
The DecisionTreeID3 companion object provides factory methods.
object DecisionTreeID3Test extends App
The DecisionTreeID3Test object is used to test the DecisionTreeID3 class.
The DecisionTreeID3Test object is used to test the DecisionTreeID3 class. Ex: Classify (No/Yes) whether a person will play tennis based on the measured features.

See also
www.cise.ufl.edu/~ddd/cap6635/Fall-97/Short-papers/2.htm > runMain scalation.analytics.classifier.DecisionTreeID3Test
object DecisionTreeID3Test2 extends App
The DecisionTreeID3Test2 object is used to test the DecisionTreeID3 class.
The DecisionTreeID3Test2 object is used to test the DecisionTreeID3 class. Ex: Classify whether a there is breast cancer. > runMain scalation.analytics.classifier.DecisionTreeID3Test2
object DecisionTreeID3Test3 extends App
The DecisionTreeID3Test3 object is used to test the DecisionTreeID3 class.
The DecisionTreeID3Test3 object is used to test the DecisionTreeID3 class. Plot entropy. > runMain scalation.analytics.classifier.DecisionTreeID3Test3
object DecisionTreeID3wp extends App
The DecisionTreeID3wp companion object provides a factory function.
object DecisionTreeID3wpTest extends App
The DecisionTreeID3wpTest object is used to test the DecisionTreeID3wp class.
The DecisionTreeID3wpTest object is used to test the DecisionTreeID3wp class. > runMain scalation.analytics.classifier.DecisionTreeID3wpTest
object DecisionTreeID3wpTest2 extends App
The DecisionTreeID3wpTest2 object is used to test the DecisionTreeID3wp class.
The DecisionTreeID3wpTest2 object is used to test the DecisionTreeID3wp class. > runMain scalation.analytics.classifier.DecisionTreeID3wpTest2
object DecisionTreeID3wpTest3 extends App
The DecisionTreeID3wpTest3 object is used to test the DecisionTreeID3wp class.
The DecisionTreeID3wpTest3 object is used to test the DecisionTreeID3wp class. > runMain scalation.analytics.classifier.DecisionTreeID3wpTest3
object DecisionTreeTest extends App
The DecisionTreeTest is used to test the DecisionTree class.
The DecisionTreeTest is used to test the DecisionTree class. > runMain scalation.analytics.classifier.DecisionTreeTest
object ExampleIris
The ExampleIris object is used to test all classifiers.
The ExampleIris object is used to test all classifiers. This is the well-known classification problem on how to classify a flower
val x = xy.sliceCol (1, 5) // columns 1, 2, 3, 4 val y = xy.col (5).toInt // column 5

See also
https://en.wikipedia.org/wiki/Iris_flower_data_set
object ExampleIrisTest extends App
The ExampleIrisTest test several classifiers on the Iris dataset.
The ExampleIrisTest test several classifiers on the Iris dataset. > runMain scalation.analytics.classifier.ExampleIrisTest
object ExampleMtcars
The ExampleMtcars object provides the Motor Trend Car Road Tests dataset (mtcars) as a combined 'xy' matrix.
The ExampleMtcars object provides the Motor Trend Car Road Tests dataset (mtcars) as a combined 'xy' matrix.

See also
https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/mtcars.html
https://gist.github.com/seankross/a412dfbd88b3db70b74b
object ExampleTennis
The ExampleTennis object is used to test all integer based classifiers.
The ExampleTennis object is used to test all integer based classifiers. This is the well-known classification problem on whether to play tennis based on given weather conditions. Applications may need to slice 'xy'.
val x = xy.sliceCol (0, 4) // columns 0, 1, 2, 3 val y = xy.col (4) // column 4

See also
euclid.nmu.edu/~mkowalcz/cs495f09/slides/lesson004.pdf
object ExampleTennisCont
The ExampleTennisCont object is used to test all integer based classifiers.
The ExampleTennisCont object is used to test all integer based classifiers. This is the well-known classification problem on whether to play tennis based on given weather conditions. Applications may need to slice 'xy'. The 'Cont' version uses continuous values for Temperature and Humidity.
val x = xy.sliceCol (0, 4) // columns 0, 1, 2, 3 val y = xy.col (4) // column 4

See also
euclid.nmu.edu/~mkowalcz/cs495f09/slides/lesson004.pdf
sefiks.com/2018/05/13/a-step-by-step-c4-5-decision-tree-example
object ExampleTennisContTest extends App
The ExampleTennisContTest test several classifiers on the Tennis dataset.
The ExampleTennisContTest test several classifiers on the Tennis dataset. Tests all classes that extend from ClassifierReal. > runMain scalation.analytics.classifier.ExampleTennisContTest
object ExampleTennisTest extends App
The ExampleTennisTest test several classifiers on the Tennis dataset.
The ExampleTennisTest test several classifiers on the Tennis dataset. Tests all classes that extend from ClassifierInt. > runMain scalation.analytics.classifier.ExampleTennisTest
object FANBayes
The FANBayes object is the companion object for the FANBayes class.
object FANBayes0
The FANBayes0 object is the companion object for the FANBayes0 class.
object FANBayesTest extends App
The FANBayesTest object is used to test the FANBayes0 and 'FANBayes' classes.
The FANBayesTest object is used to test the FANBayes0 and 'FANBayes' classes. > runMain scalation.analytics.classifier.FANBayesTest
object FANBayesTest2 extends App
The FANBayesTest2 object is used to test the FANBayes0 and FANBayes classes.
The FANBayesTest2 object is used to test the FANBayes0 and FANBayes classes. Classify whether a car is more likely to be stolen (1) or not (1).

See also
www.inf.u-szeged.hu/~ormandi/ai2/06-AugNaiveBayes-example.pdf > runMain scalation.analytics.classifier.FANBayesTest2
object FANBayesTest3 extends App
The FANBayesTest3 object is used to test the FANBayes0 and FANBayes classes.
The FANBayesTest3 object is used to test the FANBayes0 and FANBayes classes. Given whether a person is Fast and/or Strong, classify them as making C = 1 or not making C = 0 the football team. > runMain scalation.analytics.classifier.FANBayesTest3
object FANBayesTest4 extends App
The FANBayesTest4 object is used to test the FANBayes0 and FANBayes classes.
The FANBayesTest4 object is used to test the FANBayes0 and FANBayes classes. > runMain scalation.analytics.classifier.FANBayesTest4
object Filter
The Filter object provides the convolution operator.
object Filter1D
The Filter object provides the convolution operator.
object GMMTest extends App
The GMMTest object is used to test the GMM class.
The GMMTest object is used to test the GMM class. > runMain scalation.analytics.classifier.GMMTest
object HiddenMarkov
The HiddenMarkov companion object provides a convenience method for testing.
object HiddenMarkovTest extends App
The HiddenMarkovTest object is used to test the HiddenMarkov class.
The HiddenMarkovTest object is used to test the HiddenMarkov class. Given model '(pi, a, b)', determine the probability of the observations 'y'.

See also
www.cs.sjsu.edu/~stamp/RUA/HMM.pdf (exercise 1). > runMain scalation.analytics.classifier.HiddenMarkovTest
object HiddenMarkovTest2 extends App
The HiddenMarkovTest2 object is used to test the HiddenMarkov class.
The HiddenMarkovTest2 object is used to test the HiddenMarkov class. Train the model (pi, a, b) based on the observed data.

See also
www.cs.sjsu.edu/~stamp/RUA/HMM.pdf. > runMain scalation.analytics.classifier.HiddenMarkovTest2
object HiddenMarkovTest3 extends App
The HiddenMarkovTest3 object is used to test the HiddenMarkov class.
The HiddenMarkovTest3 object is used to test the HiddenMarkov class. Given model '(pi, a, b)', determine the probability of the observations 'y'.

See also
"Introduction to Data Science using ScalaTion" > runMain scalation.analytics.classifier.HiddenMarkovTest3
object HiddenMarkovTest4 extends App
The HiddenMarkovTest4 object is used to test the HiddenMarkov class.
The HiddenMarkovTest4 object is used to test the HiddenMarkov class. Train the model (pi, a, b) based on the observed data.

See also
"Introduction to Data Science using ScalaTion" > runMain scalation.analytics.classifier.HiddenMarkovTest4
object KNN_Classifier
The KNN_Classifier companion object provides a factory method.
object KNN_ClassifierTest extends App
The KNN_ClassifierTest object is used to test the KNN_Classifier class.
The KNN_ClassifierTest object is used to test the KNN_Classifier class. > runMain scalation.analytics.classifier.KNN_ClassifierTest
object KNN_ClassifierTest2 extends App
The KNN_ClassifierTest2 object is used to test the KNN_Classifier class.
The KNN_ClassifierTest2 object is used to test the KNN_Classifier class. > runMain scalation.analytics.classifier.KNN_ClassifierTest2
object KNN_ClassifierTest3 extends App
The KNN_ClassifierTest3 object is used to test the KNN_Classifier class.
The KNN_ClassifierTest3 object is used to test the KNN_Classifier class. It uses the Iris dataset where the classification/response 'y' is unbalanced. > runMain scalation.analytics.classifier.KNN_ClassifierTest3
object KNN_ClassifierTest4 extends App
The KNN_ClassifierTest4 object is used to test the KNN_Classifier class.
The KNN_ClassifierTest4 object is used to test the KNN_Classifier class. It uses the Iris dataset where the classification/response 'y' is imbalanced and downsampling is used to balance the classification. > runMain scalation.analytics.classifier.KNN_ClassifierTest4
object LDATest extends App
The LDATest is used to test the LDA class.
The LDATest is used to test the LDA class.

See also
people.revoledu.com/kardi/tutorial/LDA/Numerical%20Example.html > runMain scalation.analytics.classifier.LDATest
object LogisticRegressionTest extends App
The LogisticRegressionTest object tests the LogisticRegression class on the mtcars dataset.
The LogisticRegressionTest object tests the LogisticRegression class on the mtcars dataset.

See also
ExampleMtcars.scala
www.cookbook-r.com/Statistical_analysis/Logistic_regression/ Answer: b = (-8.8331, 0.4304), n_dev = 43.860, r_dev = 25.533, aci = 29.533, pseudo_rSq = 0.4178 > runMain scalation.analytics.classifier.LogisticRegressionTest
object LogisticRegressionTest2 extends App
The LogisticRegressionTest2 object tests the LogisticRegression class.
The LogisticRegressionTest2 object tests the LogisticRegression class.

See also
statmaster.sdu.dk/courses/st111/module03/index.html
www.stat.wisc.edu/~mchung/teaching/.../GLM.logistic.Rpackage.pdf > runMain scalation.analytics.classifier.LogisticRegressionTest2
object NaiveBayes
NaiveBayes is the companion object for the NaiveBayes class.
object NaiveBayes0
NaiveBayes0 is the companion object for the NaiveBayes0 class.
object NaiveBayesMLTest2 extends App
The NaiveBayesMLTest2 object is used to test the 'NaiveBayesML' class.
The NaiveBayesMLTest2 object is used to test the 'NaiveBayesML' class. Given whether a person is Fast and/or Strong, classify them as making C = 1 or not making C = 0 the football team. > runMain scalation.analytics.classifier.NaiveBayesMLTest2
object NaiveBayesMLTest3 extends App
The NaiveBayesMLTest3 object is used to test the 'NaiveBayesML' class.
The NaiveBayesMLTest3 object is used to test the 'NaiveBayesML' class. > runMain scalation.analytics.classifier.NaiveBayesMLTest3
object NaiveBayesR
NaiveBayesR is the companion object for the NaiveBayesR class.
object NaiveBayesRTest extends App
The NaiveBayesRTest object is used to test the NaiveBayesR class.
The NaiveBayesRTest object is used to test the NaiveBayesR class.

See also
people.revoledu.com/kardi/tutorial/LDA/Numerical%20Example.html > runMain scalation.analytics.classifier.NaiveBayesRTest
object NaiveBayesRTest2 extends App
The NaiveBayesRTest2 object is used to test the NaiveBayesR class.
The NaiveBayesRTest2 object is used to test the NaiveBayesR class. Ex: Classify whether a person is male (M) or female (F) based on the measured features.

See also
en.wikipedia.org/wiki/Naive_Bayes_classifier > runMain scalation.analytics.classifier.NaiveBayesRTest2
object NaiveBayesTest extends App
The NaiveBayesTest object is used to test the 'NaiveBayes' class.
The NaiveBayesTest object is used to test the 'NaiveBayes' class. > runMain scalation.analytics.classifier.NaiveBayesTest
object NaiveBayesTest2 extends App
The NaiveBayesTest2 object is used to test the NaiveBayes class.
The NaiveBayesTest2 object is used to test the NaiveBayes class. Classify whether a car is more likely to be stolen (1) or not (1).

See also
www.inf.u-szeged.hu/~ormandi/ai2/06-naiveBayes-example.pdf > runMain scalation.analytics.classifier.NaiveBayesTest2
object NaiveBayesTest3 extends App
The NaiveBayesTest3 object is used to test the 'NaiveBayes' class.
The NaiveBayesTest3 object is used to test the 'NaiveBayes' class. Given whether a person is Fast and/or Strong, classify them as making C = 1 or not making C = 0 the football team. > runMain scalation.analytics.classifier.NaiveBayesTest3
object NaiveBayesTest4 extends App
The NaiveBayesTest4 object is used to test the 'NaiveBayes' class.
The NaiveBayesTest4 object is used to test the 'NaiveBayes' class.

See also
archive.ics.uci.edu/ml/datasets/Lenses
docs.roguewave.com/imsl/java/7.3/manual/api/com/imsl/datamining/NaiveBayesClassifierEx2.html > runMain scalation.analytics.classifier.NaiveBayesTest4
object NaiveBayesTest5 extends App
The NaiveBayesTest5 object is used to test the 'NaiveBayes' class.
The NaiveBayesTest5 object is used to test the 'NaiveBayes' class. > runMain scalation.analytics.classifier.NaiveBayesTest5
object NaiveBayesTest6 extends App
The NaiveBayesTest6 object is used to test the 'NaiveBayes' class.
The NaiveBayesTest6 object is used to test the 'NaiveBayes' class. > runMain scalation.analytics.classifier.NaiveBayesTest6
object NaiveBayesTest7 extends App
The NaiveBayesTest7 object is used to test the 'NaiveBayes' class.
The NaiveBayesTest7 object is used to test the 'NaiveBayes' class. > runMain scalation.analytics.classifier.NaiveBayesTest7
object NeuralNet_Classif_3L extends ModelFactory
The NeuralNet_Classif_3L companion object provides factory functions for buidling three-layer (one hidden layer) neural network classifiers.
The NeuralNet_Classif_3L companion object provides factory functions for buidling three-layer (one hidden layer) neural network classifiers. Note, 'rescale' is defined in ModelFactory in Model.scala.
object NeuralNet_Classif_3LTest extends App
The NeuralNet_Classif_3LTest object is used to test the NeuralNet_Classif_3L class.
The NeuralNet_Classif_3LTest object is used to test the NeuralNet_Classif_3L class. It tests the Neural Network three layer classifier on Diabetes dataset. > runMain scalation.analytics.classifier.NeuralNet_Classif_3LTest
object NeuralNet_Classif_XL extends ModelFactory
The NeuralNet_Classif_XL companion object provides factory functions for buidling three-layer (one hidden layer) neural network classifiers.
The NeuralNet_Classif_XL companion object provides factory functions for buidling three-layer (one hidden layer) neural network classifiers. Note, 'rescale' is defined in ModelFactory in Model.scala.
object NeuralNet_Classif_XLTest extends App
The NeuralNet_Classif_XLTest object is used to test the NeuralNet_Classif_XL class.
The NeuralNet_Classif_XLTest object is used to test the NeuralNet_Classif_XL class. It tests the Neural Network three layer classifier on Diabetes dataset. > runMain scalation.analytics.classifier.NeuralNet_Classif_XLTest
object Node extends Serializable
The Node companion object provides helper functions.
object NullModelTest extends App
The NullModelTest object is used to test the NullModel class.
The NullModelTest object is used to test the NullModel class. Classify whether to play tennis(1) or not (0). > runMain scalation.analytics.classifier.NullModelTest
object OneBAN
OneBAN is the companion object for the OneBAN class.
object OneBAN0
OneBAN0 is the companion object for the OneBAN0 class.
object OneBANTest extends App
The OneBANTest object is used to test the OneBAN class.
The OneBANTest object is used to test the OneBAN class. Classify whether a car is more likely to be stolen (1) or not (1).

See also
www.inf.u-szeged.hu/~ormandi/ai2/06-OneBAN-example.pdf > runMain scalation.analytics.classifier.OneBANTest
object OneBANTest2 extends App
The OneBANTest2 object is used to test the OneBAN class.
The OneBANTest2 object is used to test the OneBAN class. Given whether a person is Fast and/or Strong, classify them as making C = 1 or not making C = 0 the football team. > runMain scalation.analytics.classifier.OneBANTest2
object OneBANTest3 extends App
The OneBANTest3 object is used to test the OneBAN class.
The OneBANTest3 object is used to test the OneBAN class. > runMain scalation.analytics.classifier.OneBANTest3
object PGMHD3
PGMHD3 is the companion object for the PGMHD3 class.
object PGMHD3Test extends App
The PGMHD3Test object is used to test the PGMHD3 class.
The PGMHD3Test object is used to test the PGMHD3 class. Classify whether a car is more likely to be stolen (1) or not (1).

See also
www.inf.u-szeged.hu/~ormandi/ai2/06-naiveBayes-example.pdf > runMain scalation.analytics.classifier.PGMHD3Test
object PGMHD3Test2 extends App
The PGMHD3Test2 object is used to test the 'PGMHD3' class.
The PGMHD3Test2 object is used to test the 'PGMHD3' class. Given whether a person is Fast and/or Strong, classify them as making C = 1 or not making C = 0 the football team. > runMain scalation.analytics.classifier.PGMHD3Test2
object PGMHD3Test3 extends App
The PGMHD3Test3 object is used to test the 'PGMHD3' class.
The PGMHD3Test3 object is used to test the 'PGMHD3' class. > runMain scalation.analytics.classifier.PGMHD3Test3
object PGMHD3cp
PGMHD3cp is the companion object for the PGMHD3cp class.
object PGMHD3cpTest extends App
The PGMHD3cpTest object is used to test the PGMHD3cp class.
The PGMHD3cpTest object is used to test the PGMHD3cp class. Classify whether a car is more likely to be stolen (1) or not (1).

See also
www.inf.u-szeged.hu/~ormandi/ai2/06-naiveBayes-example.pdf > runMain scalation.analytics.classifier.PGMHD3cpTest
object PGMHD3cpTest2 extends App
The PGMHD3cpTest2 object is used to test the 'PGMHD3cp' class.
The PGMHD3cpTest2 object is used to test the 'PGMHD3cp' class. Given whether a person is Fast and/or Strong, classify them as making C = 1 or not making C = 0 the football team. > runMain scalation.analytics.classifier.PGMHD3cpTest2
object PGMHD3cpTest3 extends App
The PGMHD3cpTest3 object is used to test the 'PGMHD3cp' class.
The PGMHD3cpTest3 object is used to test the 'PGMHD3cp' class. > runMain scalation.analytics.classifier.PGMHD3cpTest3
object PGMHD3fl
PGMHD3fl is the companion object for the PGMHD3fl class.
object PGMHD3flTest extends App
The PGMHD3flTest object is used to test the PGMHD3fl class.
The PGMHD3flTest object is used to test the PGMHD3fl class. Classify whether a car is more likely to be stolen (1) or not (1).

See also
www.inf.u-szeged.hu/~ormandi/ai2/06-naiveBayes-example.pdf > runMain scalation.analytics.classifier.PGMHD3flTest
object RandomForest2Test extends App
The RandomForest2Test object is used to test the RandomForest2 class.
The RandomForest2Test object is used to test the RandomForest2 class. It tests a simple case that does not require a file to be read. > runMain scalation.analytics.classifier.RandomForest2Test
object RandomForest2Test2 extends App
The RandomForest2Test2 object is used to test the RandomForest2 class.
The RandomForest2Test2 object is used to test the RandomForest2 class. It tests the Random Forest classifier using well-known WineQuality Dataset. > runMain scalation.analytics.classifier.RandomForest2Test2
object RandomForest2Test3 extends App
The RandomForest2Test3 object is used to test the RandomForest2 class.
The RandomForest2Test3 object is used to test the RandomForest2 class. It tests the Random Forest classifier by specific numbers of trees. > runMain scalation.analytics.classifier.RandomForest2Test3
object RandomForest2Test4 extends App
The RandomForest2Test4 object is used to test the RandomForest2 class.
The RandomForest2Test4 object is used to test the RandomForest2 class. It tests RF using unseen data. > runMain scalation.analytics.classifier.RandomForest2Test4
object RandomForest2Test5 extends App
The RandomForest2Test5 object is used to test the RandomForest2 class.
The RandomForest2Test5 object is used to test the RandomForest2 class. It tests the Random Forest classifier by specific numbers of trees. > runMain scalation.analytics.classifier.RandomForest2Test5
object RandomForest2Test6 extends App
The RandomForest2Test6 object is used to test the RandomForest2 class.
The RandomForest2Test6 object is used to test the RandomForest2 class. It tests the Random Forest classifier by specific numbers of trees. > runMain scalation.analytics.classifier.RandomForest2Test6
object RandomForest2Test7 extends App
The RandomForest2Test7 object is used to test the RandomForest2 class.
The RandomForest2Test7 object is used to test the RandomForest2 class. It tests the Random Forest classifier by specific numbers of trees. > runMain scalation.analytics.classifier.RandomForest2Test7
object RandomForestTest extends App
The RandomForestTest object is used to test the RandomForest class.
The RandomForestTest object is used to test the RandomForest class. It tests a simple case that does not require a file to be read. > runMain scalation.analytics.classifier.RandomForestTest
object RandomForestTest2 extends App
The RandomForestTest2 object is used to test the RandomForest class.
The RandomForestTest2 object is used to test the RandomForest class. It tests the Random Forest classifier using well-known WineQuality Dataset. > runMain scalation.analytics.classifier.RandomForestTest2
object RandomForestTest3 extends App
The RandomForestTest3 object is used to test the RandomForest class.
The RandomForestTest3 object is used to test the RandomForest class. It tests the Random Forest classifier by specific numbers of trees. > runMain scalation.analytics.classifier.RandomForestTest3
object RandomForestTest4 extends App
The RandomForestTest4 object is used to test the RandomForest class.
The RandomForestTest4 object is used to test the RandomForest class. It tests RF using unseen data. > runMain scalation.analytics.classifier.RandomForestTest4
object RandomForestTest5 extends App
The RandomForestTest5 object is used to test the RandomForest class.
The RandomForestTest5 object is used to test the RandomForest class. It tests the Random Forest classifier by specific numbers of trees. > runMain scalation.analytics.classifier.RandomForestTest5
object RandomForestTest6 extends App
The RandomForestTest6 object is used to test the RandomForest class.
The RandomForestTest6 object is used to test the RandomForest class. It tests the Random Forest classifier on Breast Cancer dataset. > runMain scalation.analytics.classifier.RandomForestTest6
object RandomForestTest7 extends App
The RandomForestTest7 object is used to test the RandomForest class.
The RandomForestTest7 object is used to test the RandomForest class. It tests the Random Forest classifier on Diabetes dataset. > runMain scalation.analytics.classifier.RandomForestTest7
object Round
The Round object provides methods to round double vectors and matrices into integer vectors and matrices.
object SimpleLDATest extends App
The SimpleLDATest is used to test the SimpleLDA class.
The SimpleLDATest is used to test the SimpleLDA class.

See also
people.revoledu.com/kardi/tutorial/LDA/Numerical%20Example.html > runMain scalation.analytics.classifier.SimpleLDATest
object SimpleLDATest2 extends App
The SimpleLDATest2 is used to test the SimpleLDA class.
The SimpleLDATest2 is used to test the SimpleLDA class. > runMain scalation.analytics.classifier.SimpleLDATest2
object SimpleLogisticRegression
The SimpleLogisticRegression companion object provides factory methods.
object SimpleLogisticRegressionTest extends App
The SimpleLogisticRegressionTest object tests the SimpleLogisticRegression class on the mtcars dataset.
The SimpleLogisticRegressionTest object tests the SimpleLogisticRegression class on the mtcars dataset. Use built-in optimizer.

See also
ExampleMtcars.scala
www.cookbook-r.com/Statistical_analysis/Logistic_regression/ Answer: b = (-8.8331, 0.4304), n_dev = 43.860, r_dev = 25.533, aic = 29.533, pseudo_rSq = 0.4178 > runMain scalation.analytics.classifier.SimpleLogisticRegressionTest
object SimpleLogisticRegressionTest2 extends App
The SimpleLogisticRegressionTest2 object tests the SimpleLogisticRegression class on the mtcars dataset.
The SimpleLogisticRegressionTest2 object tests the SimpleLogisticRegression class on the mtcars dataset. Use grid search optimizer. To get a better solution, refine the grid.

See also
ExampleMtcars.scala
www.cookbook-r.com/Statistical_analysis/Logistic_regression/ Answer: b = (-8.8331, 0.4304), n_dev = 43.860, r_dev = 25.533, aic = 29.533, pseudo_rSq = 0.4178 > runMain scalation.analytics.classifier.SimpleLogisticRegressionTest2
object SimpleLogisticRegressionTest3 extends App
The SimpleLogisticRegressionTest3 object tests the SimpleLogisticRegression class.
The SimpleLogisticRegressionTest3 object tests the SimpleLogisticRegression class. Compare SimpleLogisticRegressionTest with SimpleRegression.

See also
www.cookbook-r.com/Statistical_analysis/Logistic_regression/ Answer: b = (-8.8331, 0.4304), n_dev = 43.860, r_dev = 25.533, aic = 29.533, pseudo_rSq = 0.4178 > runMain scalation.analytics.classifier.SimpleLogisticRegressionTest3
object SimpleLogisticRegressionTest4 extends App
The SimpleLogisticRegressionTest4 is used to test the SimpleLogisticRegression class.
The SimpleLogisticRegressionTest4 is used to test the SimpleLogisticRegression class.

See also
people.revoledu.com/kardi/tutorial/LDA/Numerical%20Example.html > runMain scalation.analytics.classifier.SimpleLogisticRegressionTest4
object SimpleLogisticRegressionTest5 extends App
The SimpleLogisticRegressionTest5 is used to test the SimpleLogisticRegression class.
The SimpleLogisticRegressionTest5 is used to test the SimpleLogisticRegression class. > runMain scalation.analytics.classifier.SimpleLogisticRegressionTest5
object SimpleLogisticRegressionTest6 extends App
The SimpleLogisticRegressionTest6 is used to test the logistic function.
The SimpleLogisticRegressionTest6 is used to test the logistic function. > runMain scalation.analytics.classifier.SimpleLogisticRegressionTest6
object SupportVectorMachineTest extends App
The SupportVectorMachineTest is used to test the SupportVectorMachine class.
The SupportVectorMachineTest is used to test the SupportVectorMachine class. > runMain scalation.analytics.classifier.SupportVectorMachineTest
object SupportVectorMachineTest2 extends App
The SupportVectorMachineTest2 is used to test the SupportVectorMachine class.
object TANBayes
The TANBayes object is the companion object for the TANBayes class.
object TANBayes0
The TANBayes0 object is the companion object for the TANBayes0 class.
object TANBayesTest extends App
The TANBayesTest object is used to test the TANBayes0 and 'TANBayes' classes.
The TANBayesTest object is used to test the TANBayes0 and 'TANBayes' classes. > runMain scalation.analytics.classifier.TANBayesTest
object TANBayesTest2 extends App
The TANBayesTest2 object is used to test the TANBayes0 and TANBayes classes.
The TANBayesTest2 object is used to test the TANBayes0 and TANBayes classes. Classify whether a car is more likely to be stolen (1) or not (1).

See also
www.inf.u-szeged.hu/~ormandi/ai2/06-AugNaiveBayes-example.pdf > runMain scalation.analytics.classifier.TANBayesTest2
object TANBayesTest3 extends App
The TANBayesTest3 object is used to test the TANBayes0 and TANBayes classes.
The TANBayesTest3 object is used to test the TANBayes0 and TANBayes classes. Given whether a person is Fast and/or Strong, classify them as making C = 1 or not making C = 0 the football team. > runMain scalation.analytics.classifier.TANBayesTest3
object TANBayesTest4 extends App
The TANBayesTest4 object is used to test the TANBayes0 and TANBayes classes.
The TANBayesTest4 object is used to test the TANBayes0 and TANBayes classes. > runMain scalation.analytics.classifier.TANBayesTest4
object TwoBAN_OS
The TwoBAN_OS object is the companion object for the TwoBAN_OS class.
object TwoBAN_OS0
The TwoBAN_OS0 object is the companion object for the TwoBAN_OS0 class.
object TwoBAN_OSTest extends App
The TwoBAN_OSTest object is used to test the TwoBAN_OS0 class.
The TwoBAN_OSTest object is used to test the TwoBAN_OS0 class. Classify whether a car is more likely to be stolen (1) or not (1).

See also
www.inf.u-szeged.hu/~ormandi/ai2/06-TwoBAN_OS0-example.pdf > runMain scalation.analytics.classifier.TwoBAN_OSTest
object TwoBAN_OSTest2 extends App
The TwoBAN_OSTest2 object is used to test the TwoBAN_OS0 class.
The TwoBAN_OSTest2 object is used to test the TwoBAN_OS0 class. Given whether a person is Fast and/or Strong, classify them as making C = 1 or not making C = 0 the football team. > runMain scalation.analytics.classifier.TwoBAN_OSTest2
object TwoBAN_OSTest3 extends App
The TwoBAN_OSTest3 object is used to test the TwoBAN_OS0 class.
The TwoBAN_OSTest3 object is used to test the TwoBAN_OS0 class. > runMain scalation.analytics.classifier.TwoBAN_OSTest3

Packages

classifier

package classifier

Type Members

Value Members

Inherited from AnyRef

Inherited from Any

Ungrouped

Packages

classifier 

package classifier

Type Members

Value Members

Inherited from AnyRef

Inherited from Any

Ungrouped

classifier