NCC Class Reference

#include <NCC.h>

Inheritance diagram for NCC:

Public Member Functions
	NCC (bool useIterative)
void	fit (const Matrix< double > &X, const Matrix< double > &y) override
void	fit_batch (const Matrix< double > &X, const Matrix< double > &y, const Matrix< double > &labels)
void	fit_iterative (const Matrix< double > &X, const Matrix< double > &y, const Matrix< double > &labels)
Matrix< double >	predict (const Matrix< double > &x) override
Public Member Functions inherited from Classifier
	Classifier ()
void	initialize_weights (size_t numRows, size_t numColumns=1)
void	update_weights (const Matrix< double > &update, const Matrix< double > &delta)
Matrix< double >	transform (const Matrix< double > &in) override
virtual void	fit (const Matrix< double > &X, const Matrix< double > &y)=0
virtual Matrix< double >	predict (const Matrix< double > &)=0
virtual Matrix< double >	transform (const Matrix< double > &)=0

Private Attributes
bool	use_iterative = true

Additional Inherited Members
Public Attributes inherited from Classifier
Matrix< double >	weights
	Vector holding weights. More...
Matrix< double >	costs
	Vector holding classification error per epoch. More...
Protected Attributes inherited from Classifier
bool	w_initialized = false
	flag to initialize weights only once More...

Detailed Description

$: $N$ training sample lables Out: $\mu_k$: $K$ class centroids

Calculates centroids iteratively, holds $K$ centroids and 1 training sample in memory at-a-time.

Examples

ds/TestNCC.cpp.

Constructor & Destructor Documentation

NCC()

NCC::NCC ( bool  useIterative )

inline

Member Function Documentation

fit()

void NCC::fit ( const Matrix< double > &  X, const Matrix< double > &  y  )

inlineoverridevirtual

Implements training algorithm

Parameters

X	array-like with the shape: [n_samples, n_features]
y	array-like with shape: [n_samples, 1]

Returns

this

Implements Predictor.

fit_batch()

void NCC::fit_batch ( const Matrix< double > &  X, const Matrix< double > &  y, const Matrix< double > &  labels  )

inline

Batch-wise implementation of nearest centroid classifier centroid calculation.

Note: Requires all input data in memory.

Complexity: O(K * 6N)

Parameters

X	given input data to calculate centroids from
y	given labels for input data X
labels	unique labels inside y

fit_iterative()

void NCC::fit_iterative ( const Matrix< double > &  X, const Matrix< double > &  y, const Matrix< double > &  labels  )

inline

Iterative implementation of nearest centroid classifier centroid calculation

Note: Allows streaming of input data.

Complexity: O(N * 3D)

Parameters

X	input data to calculate centroids from
y	given labels for input data X
labels	unique labels inside y

predict()

Matrix< double > NCC::predict ( const Matrix< double > &  x )

inlineoverridevirtual

Prediction: In: $x$: Data point to classify $\mu_k$: $k$ class centroids Out: $$ argmin(||\mu_k - x||_2) $$

Implements Predictor.

Member Data Documentation

use_iterative

bool NCC::use_iterative = true

private

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The documentation for this class was generated from the following file:

• include/math/ds/NCC.h