class KNN_Predictor extends PredictorMat
The KNN_Predictor class is used to predict a response value for new vector 'z'.
It works by finding its 'kappa' nearest neighbors. These neighbors essentially
vote according to their prediction. The consensus is the average individual
predictions for 'z'. Using a distance metric, the 'kappa' vectors nearest
to 'z' are found in the training data, which are stored row-wise in data
matrix 'x'. The corresponding response values are given in the vector 'y',
such that the response value for vector 'x(i)' is given by 'y(i)'.
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- KNN_Predictor
- PredictorMat
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- Predictor
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Instance Constructors
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new
KNN_Predictor(x: MatriD, y: VectoD, fname_: Strings = null, hparam: HyperParameter = KNN_Predictor.hp)
- x
the vectors/points of predictor data stored as rows of a matrix
- y
the response value for each vector in x
- fname_
the names for all features/variables
- hparam
the number of nearest neighbors to consider
Value Members
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final
def
!=(arg0: Any): Boolean
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final
def
##(): Int
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final
def
==(arg0: Any): Boolean
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final
def
asInstanceOf[T0]: T0
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val
b: VectoD
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- Predictor
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def
clone(): AnyRef
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def
crossVal(k: Int, rando: Boolean): Unit
The 'crossVal' abstract method must be coded in implementing classes to call the above 'crossValidate' method.
The 'crossVal' abstract method must be coded in implementing classes to call the above 'crossValidate' method. The 'algor' parameter may be specified as a lambda function to create the prediction algorithm.
- k
the number of crosses and cross-validations (defaults to 10x).
- rando
flag for using randomized cross-validation
- Definition Classes
- KNN_Predictor → PredictorMat
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def
crossValidate(algor: (MatriD, VectoD) ⇒ PredictorMat, k: Int = 10, rando: Boolean = true): Array[Statistic]
- Definition Classes
- PredictorMat
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def
diagnose(e: VectoD, w: VectoD = null, yp: VectoD = null, y_: VectoD = y): Unit
Given the error/residual vector, compute the quality of fit measures.
Given the error/residual vector, compute the quality of fit measures.
- e
the corresponding m-dimensional error vector (y - yp)
- w
the weights on the instances
- yp
the predicted response vector (x * b)
- Definition Classes
- Fit
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def
distance(x: VectoD, z: VectoD): Double
Compute a distance metric between vectors/points 'x' and 'z'.
Compute a distance metric between vectors/points 'x' and 'z'. The squared Euclidean norm used for efficiency, but may use other norms.
- x
the first vector/point
- z
the second vector/point
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val
e: VectoD
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- Predictor
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final
def
eq(arg0: AnyRef): Boolean
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def
equals(arg0: Any): Boolean
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def
eval(xx: MatriD, yy: VectoD): Unit
Evaluate by diagnose the error.
Evaluate by diagnose the error.
- xx
the data matrix used in prediction
- yy
the actual response vector
- Definition Classes
- KNN_Predictor → PredictorMat → Predictor
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def
eval(): Unit
Evaluate by diagnose the error.
Evaluate by diagnose the error.
- Definition Classes
- KNN_Predictor → PredictorMat → Predictor
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def
finalize(): Unit
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def
fit: VectoD
Return the quality of fit including 'rSq', 'sst', 'sse', 'mse0', rmse', 'mae', 'df._2', 'rBarSq', 'fStat', 'aic', 'bic'.
Return the quality of fit including 'rSq', 'sst', 'sse', 'mse0', rmse', 'mae', 'df._2', 'rBarSq', 'fStat', 'aic', 'bic'. Note, if 'sse > sst', the model introduces errors and the 'rSq' may be negative, otherwise, R^2 ('rSq') ranges from 0 (weak) to 1 (strong). Note that 'rSq' is the number 5 measure. Override to add more quality of fit measures.
- Definition Classes
- Fit
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def
fitLabel: Seq[String]
Return the labels for the quality of fit measures.
Return the labels for the quality of fit measures. Override to add more quality of fit measures.
- Definition Classes
- Fit
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def
fitMap: Map[String, String]
Build a map of quality of fit measures (use of
LinedHashMapmakes it ordered).Build a map of quality of fit measures (use of
LinedHashMapmakes it ordered). Override to add more quality of fit measures.- Definition Classes
- Fit
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final
def
flaw(method: String, message: String): Unit
- Definition Classes
- Error
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var
fname: Strings
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- protected
- Definition Classes
- PredictorMat
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final
def
getClass(): Class[_]
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def
hashCode(): Int
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def
hparameter: HyperParameter
Return the hyper-parameters.
Return the hyper-parameters.
- Definition Classes
- PredictorMat
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val
index_rSq: Int
- Definition Classes
- Fit
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final
def
isInstanceOf[T0]: Boolean
- Definition Classes
- Any
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val
k: Int
- Attributes
- protected
- Definition Classes
- PredictorMat
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def
kNearest(z: VectoD): Unit
Find the 'kappa' nearest neighbors (top-'kappa') to vector 'z' and store in the 'topK' array.
Find the 'kappa' nearest neighbors (top-'kappa') to vector 'z' and store in the 'topK' array. Break ties by flipping a fair coin.
- z
the vector used for prediction
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val
m: Int
- Attributes
- protected
- Definition Classes
- PredictorMat
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def
mse_: Double
Return the mean of squares for error (sse / df._2).
Return the mean of squares for error (sse / df._2). Must call diagnose first.
- Definition Classes
- Fit
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final
def
ne(arg0: AnyRef): Boolean
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final
def
notify(): Unit
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final
def
notifyAll(): Unit
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def
parameter: VectoD
Return the vector of parameter/coefficient values.
Return the vector of parameter/coefficient values.
- Definition Classes
- Predictor
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def
predict(z: VectoD): Double
Given a new point/vector 'z', predict its response value based on the actual response values of its 'kappa' nearest neighbors.
Given a new point/vector 'z', predict its response value based on the actual response values of its 'kappa' nearest neighbors.
- z
the vector to predict
- Definition Classes
- KNN_Predictor → PredictorMat → Predictor
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def
predict(z: MatriD = x): VectoD
Predict the value of 'y = f(z)' by evaluating the formula 'y = b dot z', for each row of matrix 'z'.
Predict the value of 'y = f(z)' by evaluating the formula 'y = b dot z', for each row of matrix 'z'.
- z
the new matrix to predict
- Definition Classes
- PredictorMat
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def
predict(z: VectoI): Double
Given a new discrete data vector z, predict the y-value of f(z).
Given a new discrete data vector z, predict the y-value of f(z).
- z
the vector to use for prediction
- Definition Classes
- Predictor
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def
reset(): Unit
Reset or re-initialize 'topK' and counters.
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def
resetDF(df_update: (Double, Double)): Unit
Reset the degrees of freedom to the new updated values.
Reset the degrees of freedom to the new updated values. For some models, the degrees of freedom is not known until after the model is built.
- df_update
the updated degrees of freedom
- Definition Classes
- Fit
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def
residual: VectoD
Return the vector of residuals/errors.
Return the vector of residuals/errors.
- Definition Classes
- Predictor
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def
sumCoeff(b: VectoD, stdErr: VectoD = null): String
Produce the summary report portion for the cofficients.
Produce the summary report portion for the cofficients.
- b
the parameters/coefficients for the model
- Definition Classes
- Fit
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def
summary(): String
Compute and return summary diagostics for the regression model.
Compute and return summary diagostics for the regression model.
- Definition Classes
- PredictorMat
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def
summary(b: VectoD, stdErr: VectoD = null, show: Boolean = false): String
Produce a summary report with diagnostics for each predictor 'x_j' and the overall quality of fit.
Produce a summary report with diagnostics for each predictor 'x_j' and the overall quality of fit.
- b
the parameters/coefficients for the model
- show
flag indicating whether to print the summary
- Definition Classes
- Fit
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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def
toString(): String
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def
train(itest: IndexedSeq[Int]): KNN_Predictor
Training involves resetting the data structures before each prediction.
Training involves resetting the data structures before each prediction. It uses lazy training, so most of it is done during prediction.
- itest
the indices of the test data
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def
train(yy: VectoD): KNN_Predictor
Training involves resetting the data structures before each prediction.
Training involves resetting the data structures before each prediction. It uses lazy training, so most of it is done during prediction.
- yy
the response values
- Definition Classes
- KNN_Predictor → PredictorMat → Predictor
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def
train(): PredictorMat
Given a set of data vectors 'x's and their corresponding responses 'y's, passed into the implementing class, train the prediction function 'y = f(x)' by fitting its parameters.
Given a set of data vectors 'x's and their corresponding responses 'y's, passed into the implementing class, train the prediction function 'y = f(x)' by fitting its parameters.
- Definition Classes
- PredictorMat
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def
train2(yy: VectoD = y): PredictorMat
- Definition Classes
- PredictorMat
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final
def
wait(): Unit
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final
def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
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val
x: MatriD
- Attributes
- protected
- Definition Classes
- PredictorMat
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val
y: VectoD
- Attributes
- protected
- Definition Classes
- PredictorMat