Hi Chrisophe,
You are correct, the doall also solves the problem.
Based on that, I moved the doall out of matrixMultiply and into the
computeActualResponse function, so that the caller can decide whether
they want lazy evaluation for matrixMultiply or not:
(defn computeActualResponse [signumFunction weights inputs]
(if (and (not (nil? weights)) (not (nil? inputs)))
;;TODO create a function that will apply first to a collection
until the inner item is obtained
(signumFunction (first (first (doall (matrixMultiply (transpose
weights) inputs)))))))
On Apr 21, 1:45 am, Christophe Grand <[email protected]> wrote:
> Hello,
>
> (def lazy-identity [x] (lazy-seq x))
> (nth (iterate lazy-identity [1 2]) 10) ; returns (1 2)
> (nth (iterate lazy-identity [1 2]) 1000) ; returns (1 2)
> (nth (iterate lazy-identity [1 2]) 100000) ; (with my JVM settings)
> throws a StackOverflowException
>
> Each time that you are building a lazy sequence from another one (eg
> through map, remove, filter etc.) you are building a closure that will
> call the inner closure of the "source" lazy seq.
> So when you call map on the result of a map on the result of a map on
> the result of a map on the result of a map on the result of a map on the
> result of a map on the result of a map...
> you are creating a closure that will call a closure that will call a
> closure that will call a closure that will call a closure that will call
> a closure that will call a closure... and, if they are too nested, will
> throw a StackOverflowException.
>
> However the call to the inner closure of a lazy seq takes only place if
> the seq hasn't been realized before. That's why Dimiters's suggestion
> corrects your problem.
>
> This may be another way (I didn't test it) to force the realization of
> your matrix:
>
> (defn matrixMultiply [matrixA matrixB]
> (doall
> (map
> (fn [row]
> (apply map
> (fn [& column]
> (apply + (map * row column)))
> matrixB))
> matrixA)))
>
> hth,
>
> Christophe
>
> jleehurt a écrit :
>
>
>
> > Hi Dimiter,
>
> > Thank you! I'm still a bit confused as to why this was happening. Does
> > lazy evaluation not work well with recursion?
>
> > On Apr 20, 11:06 pm, "Dimiter \"malkia\" Stanev" <[email protected]>
> > wrote:
>
> >> 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?
>
> --
> Professional:http://cgrand.net/(fr)
> On Clojure:http://clj-me.blogspot.com/(en)
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