I blindly tried printing out stuff from matrixMultiply, and found out
that if I print matrixA and matrixB it doesn't run out of stack, so I
guess I was "forcing" them to "work", here is a version with (dorun)
that has the same side effect, without printing:
(defn matrixMultiply [matrixA matrixB]
(dorun matrixA)
(dorun matrixB)
(map
(fn [row]
(apply map
(fn [& column]
(apply + (map * row column)))
matrixB))
matrixA))
user> (main 100000)
((0.5 50000.5))
user> (main 1000000)
((0.5 500000.5))
user> (time (main 1000000))
"Elapsed time: 8314.617 msecs"
((0.5 500000.5))
user> (time (main 10000000))
; Evaluation aborted. ;; Actually not stack overflow, but HEAP
overflow (it took a while though)
user>
Thanks,
Dimiter "malkia" Stanev.
On Apr 20, 10:01 pm, jleehurt <[email protected]> wrote:
> Hi David,
>
> Those two are not recursive, but they call into other functions that
> are. Do I need to make sure that all recursive functions use the loop/
> recur pattern? Or should I not nest recursive calls like this?
>
> Here is the whole source:
>
> ;; Neuron Activation Functions
>
> ;threshold
> (defn threshold [x] (if (>= x 0) 1 0))
>
> ;signum (threshold)
> (defn signum [x] (cond (> x 0) 1 (= x 0) 0 (< x 0) -1))
>
> ;; Matrix Functions
>
> (defn transpose [matrix]
> (if (not (nil? matrix))
> (apply map list matrix)))
>
> (defn transpose2 [matrix]
> (apply map (fn [& column] column) matrix))
>
> (defn matrixMultiply [matrixA matrixB]
> (map
> (fn [row] (apply map (fn [& column] (apply + (map * row column)))
> matrixB))
> matrixA))
>
> (defn matrixAdd [matrixA matrixB]
> (if (and (not (empty? matrixA)) (not (empty? matrixB)))
> (conj
> (matrixAdd (rest matrixA) (rest matrixB))
> (map + (first matrixA) (first matrixB)))))
>
> (defn matrixMultiplyScalar [matrixA scalar]
> (if (not (empty? matrixA))
> (conj
> (matrixMultiplyScalar (rest matrixA) scalar)
> (map (fn [arg] (* arg scalar)) (first matrixA)))))
>
> ;; Vector Functions
>
> (defn transposeVector [v]
> (if (not (nil? v))
> (transpose (vector v))))
>
> (defn vectorMultiplyScalar [v scalar]
> (map * v (cycle [ scalar ])))
>
> ;; Binary Logic Input/Output
>
> (def infiniteInputCollection (cycle [[[-1 -1]] [[-1 1]] [[1 -1]] [[1
> 1]]]))
> (def infiniteAndOutputCollection (cycle [-1 -1 -1 1]))
>
> (defn buildInputs [numberOfInputs]
> (loop [inputVector []
> binaryInputCollection infiniteInputCollection
> remainingCount numberOfInputs]
> (if (> 0 remainingCount)
> inputVector
> (recur
> (conj inputVector (first binaryInputCollection)) (rest
> binaryInputCollection) (dec remainingCount)))))
>
> (defn buildOutputs [numberOfOutputs outputCollection]
> (loop [outputVector []
> andOutputCollection outputCollection
> remainingCount numberOfOutputs]
> (if (> 0 remainingCount)
> outputVector
> (recur (conj outputVector (first andOutputCollection))
> (rest andOutputCollection) (dec remainingCount)))))
>
> ;; Main
>
> ;learning rate parameter eta
> (def learningRateParameter 0.5)
>
> ;the weight vector of the perceptron
> (def weightVector (ref nil))
>
> ;multiply the transpose of the weight vector with the input vector
> ;apply the signum function to the scalar result
> (defn computeActualResponse [signumFunction weights inputs]
> (if (and (not (nil? weights)) (not (nil? inputs)))
> (signumFunction (first (first (matrixMultiply (transpose
> weights) inputs))))))
>
> ;return an updated weight vector of the perceptron
> (defn getAdaptedWeightVector [weights inputs desiredResponse
> actualResponse]
> (let [etaDeltaDesiredActual (* learningRateParameter (-
> desiredResponse actualResponse))]
> (matrixAdd weights (matrixMultiplyScalar inputs
> etaDeltaDesiredActual))))
>
> ;train the perceptron with the inputs and corresponding known outputs
> (defn trainPerceptron [beginningWeightVector allInputs allOutputs]
> (loop [weightVector beginningWeightVector
> inputs allInputs
> responses allOutputs]
> (if (and (not (empty? inputs)) (not (empty? responses)))
> (let [adaptedWeightVector
> (getAdaptedWeightVector
> weightVector
> (first inputs)
> (first responses)
> (computeActualResponse signum weightVector (first
> inputs)))]
> (recur adaptedWeightVector (rest inputs) (rest
> responses)))
> weightVector)))
>
> (defn main [sizeOfDataSet]
> (let [weights [[0 0]]
> inputs (buildInputs sizeOfDataSet)
> outputs (buildOutputs sizeOfDataSet
> infiniteAndOutputCollection)]
> (trainPerceptron weights inputs outputs)))
>
> On Apr 20, 6:32 am, David Nolen <[email protected]> wrote:> You have two
> other function calls
> > getAdaptedWeightVector
> > computeActualResponse
>
> > Are these recursive as well?
>
> > On Sun, Apr 19, 2009 at 11:26 PM, jleehurt <[email protected]> wrote:
>
> > > Hi all, I have the following code that trains a perceptron with the
> > > given inputs and corresponding desired inputs. For input/output
> > > vectors, when the size gets to about 2000, I am getting a
> > > java.lang.StackOverflowError in the following function:
>
> > > (defn trainPerceptron [beginningWeightVector allInputs allOutputs]
> > > (loop [weightVector beginningWeightVector
> > > inputs allInputs
> > > responses allOutputs]
> > > (if (and (not (empty? inputs)) (not (empty? responses)))
> > > (let [adaptedWeightVector
> > > (getAdaptedWeightVector
> > > weightVector
> > > (first inputs)
> > > (first responses)
> > > (computeActualResponse signum weightVector (first
> > > inputs)))]
> > > (recur adaptedWeightVector (rest inputs) (rest
> > > responses)))
> > > weightVector)))
>
> > > Is not the purpose of loop/recur to avoid stack overflow problems?
> > > What am I doing wrong?
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