class LogisticRegression extends ClassifierReal
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
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
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new
LogisticRegression(x: MatrixD, y: VectorI, fn: Array[String], cn: Array[String] = Array ("no", "yes"))
- x
the input/design matrix augmented with a first column of ones
- y
the binary response vector, y_i in {0, 1}
- fn
the names for all factors
- cn
the names for both classes
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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
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def
classify(z: VectoD): (Int, String, Double)
Classify the value of y = f(z) by evaluating the formula y = sigmoid (b dot z).
Classify the value of y = f(z) by evaluating the formula y = sigmoid (b dot z). Return the best class, its name and FIX.
- z
the new vector to classify
- Definition Classes
- LogisticRegression → Classifier
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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
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- ClassifierReal → Classifier
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def
clone(): AnyRef
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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
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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).
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- Classifier
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eq(arg0: AnyRef): Boolean
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equals(arg0: Any): Boolean
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finalize(): Unit
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def
fit: (VectorD, Double, Double, Double, Double)
Return the fit (parameter vector b, quality of fit).
Return the fit (parameter vector b, quality of fit). Assumes both train_null and train have already been called.
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def
flaw(method: String, message: String): Unit
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getClass(): Class[_]
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hashCode(): Int
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def
ll(b: VectorD): Double
For a given parameter vector 'b', compute '-2 * Log-Likelihood (-2LL)'.
For a given parameter vector 'b', compute '-2 * Log-Likelihood (-2LL)'. '-2LL' is the standard measure that follows a Chi-Square distribution.
- b
the parameters to fit
- See also
www.statisticalhorizons.com/wp-content/uploads/Allison.StatComp.pdf
www.stat.cmu.edu/~cshalizi/350/lectures/26/lecture-26.pdf
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def
ll_null(b: VectorD): Double
For a given parameter vector 'b = [b(0)]', compute '-2 * Log-Likelihood (-2LL)'.
For a given parameter vector 'b = [b(0)]', compute '-2 * Log-Likelihood (-2LL)'. '-2LL' is the standard measure that follows a Chi-Square distribution.
- b
the parameters to fit
- See also
www.statisticalhorizons.com/wp-content/uploads/Allison.StatComp.pdf
www.stat.cmu.edu/~cshalizi/350/lectures/26/lecture-26.pdf
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val
m: Int
the number of data vectors in training-set (# rows)
the number of data vectors in training-set (# rows)
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- ClassifierReal
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val
md: Double
the training-set size as a Double
the training-set size as a Double
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- ClassifierReal
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val
n: Int
the number of features/variables (# columns)
the number of features/variables (# columns)
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- ClassifierReal
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val
nd: Double
the feature-set size as a Double
the feature-set size as a Double
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- protected
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- ClassifierReal
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final
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ne(arg0: AnyRef): Boolean
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def
notify(): Unit
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final
def
notifyAll(): Unit
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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
- LogisticRegression → Classifier
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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
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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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
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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
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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
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def
toString(): String
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def
train(testStart: Int, testEnd: Int): Unit
For the full model, train the classifier by fitting the parameter vector (b-vector) in the logistic regression equation using maximum likelihood.
For the full model, train the classifier by fitting the parameter vector (b-vector) in the logistic regression equation using maximum likelihood. Do this by minimizing '-2LL'. FIX: Use improved BFGS implementation or IRWLS
- testStart
starting index of test region (inclusive) used in cross-validation.
- testEnd
ending index of test region (exclusive) used in cross-validation.
- Definition Classes
- LogisticRegression → Classifier
- See also
en.wikipedia.org/wiki/Iteratively_reweighted_least_squares
stats.stackexchange.com/questions/81000/calculate-coefficients-in-a-logistic-regression-with-r
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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
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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
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def
train_null(): Unit
For the null model, train the classifier by fitting the parameter vector (b-vector) in the logistic regression equation using maximum likelihood.
For the null model, train the classifier by fitting the parameter vector (b-vector) in the logistic regression equation using maximum likelihood. Do this by minimizing -2LL.
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wait(): Unit
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wait(arg0: Long, arg1: Int): Unit
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