class KNN_Classifier extends ClassifierReal
The KNN_Classifier class is used to classify a new vector 'z' into one of
'k' classes. It works by finding its 'knn' 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 'knn' 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)'.
- Alphabetic
- By Inheritance
- KNN_Classifier
- ClassifierReal
- Error
- Classifier
- AnyRef
- Any
- Hide All
- Show All
- Public
- All
Instance Constructors
-
new
KNN_Classifier(x: MatriD, y: VectoI, fn: Array[String], k: Int, cn: Array[String], knn: Int = 3)
- x
the vectors/points of classified data stored as rows of a matrix
- y
the classification of each vector in x
- fn
the names for all features/variables
- k
the number of classes
- cn
the names for all classes
- knn
the number of nearest neighbors to consider
Value Members
-
final
def
!=(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
final
def
##(): Int
- Definition Classes
- AnyRef → Any
-
final
def
==(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
final
def
asInstanceOf[T0]: T0
- Definition Classes
- Any
-
def
calcCorrelation: MatriD
Calculate the correlation matrix for the feature vectors 'fea'.
Calculate the correlation matrix for the feature vectors 'fea'. If the correlations are too high, the independence assumption may be dubious.
- Definition Classes
- ClassifierReal
-
def
classify(z: VectoD): (Int, String, Double)
Given a new point/vector 'z', determine which class it belongs to (i.e., the class getting the most votes from its 'knn' nearest neighbors.
Given a new point/vector 'z', determine which class it belongs to (i.e., the class getting the most votes from its 'knn' nearest neighbors. Return the best class, its name and its votes
- z
the vector to classify
- Definition Classes
- KNN_Classifier → Classifier
-
def
classify(z: VectoI): (Int, String, Double)
Given a new discrete (integer-valued) data vector 'z', determine which class it belongs to, by first converting it to a vector of doubles.
Given a new discrete (integer-valued) data vector 'z', determine which class it belongs to, by first converting it to a vector of doubles. Return the best class, its name and its relative probability
- z
the vector to classify
- Definition Classes
- ClassifierReal → Classifier
-
def
clone(): AnyRef
- Attributes
- protected[java.lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
def
crossValidate(nx: Int = 10): Double
Test the accuracy of the classified results by cross-validation, returning the accuracy.
Test the accuracy of the classified results by cross-validation, returning the accuracy. The "test data" starts at 'testStart' and ends at 'testEnd', the rest of the data is "training data'.
- nx
the number of crosses and cross-validations (defaults to 5x).
- Definition Classes
- Classifier
-
def
crossValidateRand(nx: Int = 10): Double
Test the accuracy of the classified results by cross-validation, returning the accuracy.
Test the accuracy of the classified results by cross-validation, returning the accuracy. This version of cross-validation relies on "subtracting" frequencies from the previously stored global data to achieve efficiency.
- nx
number of crosses and cross-validations (defaults to 10x).
- Definition Classes
- Classifier
-
def
distance(u: VectoD, v: VectoD): Double
Compute a distance metric between vectors/points u and v.
Compute a distance metric between vectors/points u and v.
- u
the first vector/point
- v
the second vector/point
-
final
def
eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
def
equals(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
def
finalize(): Unit
- Attributes
- protected[java.lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( classOf[java.lang.Throwable] )
-
final
def
flaw(method: String, message: String): Unit
- Definition Classes
- Error
-
final
def
getClass(): Class[_]
- Definition Classes
- AnyRef → Any
-
def
hashCode(): Int
- Definition Classes
- AnyRef → Any
-
final
def
isInstanceOf[T0]: Boolean
- Definition Classes
- Any
-
def
kNearest(z: VectoD): Unit
Find the 'knn' nearest neighbors (top-'knn') to vector 'z' and store in the 'topK' array.
Find the 'knn' nearest neighbors (top-'knn') to vector 'z' and store in the 'topK' array.
- z
the vector to be classified
-
val
m: Int
the number of data vectors in training-set (# rows)
the number of data vectors in training-set (# rows)
- Attributes
- protected
- Definition Classes
- ClassifierReal
-
val
md: Double
the training-set size as a Double
the training-set size as a Double
- Attributes
- protected
- Definition Classes
- ClassifierReal
-
val
n: Int
the number of features/variables (# columns)
the number of features/variables (# columns)
- Attributes
- protected
- Definition Classes
- ClassifierReal
-
val
nd: Double
the feature-set size as a Double
the feature-set size as a Double
- Attributes
- protected
- Definition Classes
- ClassifierReal
-
final
def
ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
final
def
notify(): Unit
- Definition Classes
- AnyRef
-
final
def
notifyAll(): Unit
- Definition Classes
- AnyRef
-
def
reset(): Unit
Reset or re-initialize the frequency tables and the probability tables.
Reset or re-initialize the frequency tables and the probability tables.
- Definition Classes
- KNN_Classifier → Classifier
-
def
size: Int
Return the number of data vectors in training/test-set (# rows).
Return the number of data vectors in training/test-set (# rows).
- Definition Classes
- ClassifierReal → Classifier
-
final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
-
def
test(xx: MatrixD, yy: VectorI): Double
Test the quality of the training with a test-set and return the fraction of correct classifications.
Test the quality of the training with a test-set and return the fraction of correct classifications.
- xx
the real-valued test vectors stored as rows of a matrix
- yy
the test classification vector, where 'yy_i = class for row i of xx'
- Definition Classes
- ClassifierReal
-
def
test(testStart: Int, testEnd: Int): Double
Test the quality of the training with a test-set and return the fraction of correct classifications.
Test the quality of the training with a test-set and return the fraction of correct classifications.
- testStart
beginning of test region (inclusive)
- testEnd
end of test region (exclusive)
- Definition Classes
- ClassifierReal → Classifier
-
def
test(itest: VectorI): Double
Test the quality of the training with a test-set and return the fraction of correct classifications.
Test the quality of the training with a test-set and return the fraction of correct classifications.
- itest
the indices of the instances considered test data
- Definition Classes
- Classifier
-
def
toString(): String
- Definition Classes
- AnyRef → Any
-
def
train(testStart: Int, testEnd: Int): Unit
Training involves resetting the data structures before each classification.
Training involves resetting the data structures before each classification. It uses lazy training, so most of it is done during classification.
- testStart
starting index of test region (inclusive) used in cross-validation.
- testEnd
ending index of test region (exclusive) used in cross-validation.
- Definition Classes
- KNN_Classifier → Classifier
-
def
train(): Unit
Given a set of data vectors and their classifications, build a classifier.
Given a set of data vectors and their classifications, build a classifier.
- Definition Classes
- Classifier
-
def
train(itest: IndexedSeq[Int]): Unit
Given a set of data vectors and their classifications, build a classifier.
Given a set of data vectors and their classifications, build a classifier.
- itest
the indices of the instances considered as testing data
- Definition Classes
- Classifier
-
final
def
wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )