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author | Zheng RuiFeng <ruifengz@foxmail.com> | 2016-05-15 15:59:49 +0100 |
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committer | Sean Owen <sowen@cloudera.com> | 2016-05-15 15:59:49 +0100 |
commit | c7efc56c7b6fc99c005b35c335716ff676856c6c (patch) | |
tree | 09412bc7bec3167e8ec15dc8293043e1ab7abbc5 /mllib/src | |
parent | f5576a052da0bb59343bc2a6b6ce06c6abaac75b (diff) | |
download | spark-c7efc56c7b6fc99c005b35c335716ff676856c6c.tar.gz spark-c7efc56c7b6fc99c005b35c335716ff676856c6c.tar.bz2 spark-c7efc56c7b6fc99c005b35c335716ff676856c6c.zip |
[MINOR] Fix Typos
## What changes were proposed in this pull request?
1,Rename matrix args in BreezeUtil to upper to match the doc
2,Fix several typos in ML and SQL
## How was this patch tested?
manual tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes #13078 from zhengruifeng/fix_ann.
Diffstat (limited to 'mllib/src')
-rw-r--r-- | mllib/src/main/scala/org/apache/spark/ml/ann/BreezeUtil.scala | 33 | ||||
-rw-r--r-- | mllib/src/main/scala/org/apache/spark/ml/ann/Layer.scala | 36 |
2 files changed, 36 insertions, 33 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/ml/ann/BreezeUtil.scala b/mllib/src/main/scala/org/apache/spark/ml/ann/BreezeUtil.scala index 7429f9d652..6bbe7e1cb2 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/ann/BreezeUtil.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/ann/BreezeUtil.scala @@ -26,38 +26,39 @@ import com.github.fommil.netlib.BLAS.{getInstance => NativeBLAS} private[ann] object BreezeUtil { // TODO: switch to MLlib BLAS interface - private def transposeString(a: BDM[Double]): String = if (a.isTranspose) "T" else "N" + private def transposeString(A: BDM[Double]): String = if (A.isTranspose) "T" else "N" /** * DGEMM: C := alpha * A * B + beta * C * @param alpha alpha - * @param a A - * @param b B + * @param A A + * @param B B * @param beta beta - * @param c C + * @param C C */ - def dgemm(alpha: Double, a: BDM[Double], b: BDM[Double], beta: Double, c: BDM[Double]): Unit = { + def dgemm(alpha: Double, A: BDM[Double], B: BDM[Double], beta: Double, C: BDM[Double]): Unit = { // TODO: add code if matrices isTranspose!!! - require(a.cols == b.rows, "A & B Dimension mismatch!") - require(a.rows == c.rows, "A & C Dimension mismatch!") - require(b.cols == c.cols, "A & C Dimension mismatch!") - NativeBLAS.dgemm(transposeString(a), transposeString(b), c.rows, c.cols, a.cols, - alpha, a.data, a.offset, a.majorStride, b.data, b.offset, b.majorStride, - beta, c.data, c.offset, c.rows) + require(A.cols == B.rows, "A & B Dimension mismatch!") + require(A.rows == C.rows, "A & C Dimension mismatch!") + require(B.cols == C.cols, "A & C Dimension mismatch!") + NativeBLAS.dgemm(transposeString(A), transposeString(B), C.rows, C.cols, A.cols, + alpha, A.data, A.offset, A.majorStride, B.data, B.offset, B.majorStride, + beta, C.data, C.offset, C.rows) } /** * DGEMV: y := alpha * A * x + beta * y * @param alpha alpha - * @param a A + * @param A A * @param x x * @param beta beta * @param y y */ - def dgemv(alpha: Double, a: BDM[Double], x: BDV[Double], beta: Double, y: BDV[Double]): Unit = { - require(a.cols == x.length, "A & b Dimension mismatch!") - NativeBLAS.dgemv(transposeString(a), a.rows, a.cols, - alpha, a.data, a.offset, a.majorStride, x.data, x.offset, x.stride, + def dgemv(alpha: Double, A: BDM[Double], x: BDV[Double], beta: Double, y: BDV[Double]): Unit = { + require(A.cols == x.length, "A & x Dimension mismatch!") + require(A.rows == y.length, "A & y Dimension mismatch!") + NativeBLAS.dgemv(transposeString(A), A.rows, A.cols, + alpha, A.data, A.offset, A.majorStride, x.data, x.offset, x.stride, beta, y.data, y.offset, y.stride) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/ann/Layer.scala b/mllib/src/main/scala/org/apache/spark/ml/ann/Layer.scala index 3588ac1e95..a27ee51874 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/ann/Layer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/ann/Layer.scala @@ -64,8 +64,9 @@ private[ann] trait Layer extends Serializable { * @return the layer model */ def createModel(initialWeights: BDV[Double]): LayerModel + /** - * Returns the instance of the layer with random generated weights + * Returns the instance of the layer with random generated weights. * * @param weights vector for weights initialization, must be equal to weightSize * @param random random number generator @@ -83,11 +84,11 @@ private[ann] trait LayerModel extends Serializable { val weights: BDV[Double] /** - * Evaluates the data (process the data through the layer) + * Evaluates the data (process the data through the layer). * Output is allocated based on the size provided by the - * LayerModel implementation and the stack (batch) size + * LayerModel implementation and the stack (batch) size. * Developer is responsible for checking the size of output - * when writing to it + * when writing to it. * * @param data data * @param output output (modified in place) @@ -95,11 +96,11 @@ private[ann] trait LayerModel extends Serializable { def eval(data: BDM[Double], output: BDM[Double]): Unit /** - * Computes the delta for back propagation + * Computes the delta for back propagation. * Delta is allocated based on the size provided by the - * LayerModel implementation and the stack (batch) size + * LayerModel implementation and the stack (batch) size. * Developer is responsible for checking the size of - * prevDelta when writing to it + * prevDelta when writing to it. * * @param delta delta of this layer * @param output output of this layer @@ -108,10 +109,10 @@ private[ann] trait LayerModel extends Serializable { def computePrevDelta(delta: BDM[Double], output: BDM[Double], prevDelta: BDM[Double]): Unit /** - * Computes the gradient - * cumGrad is a wrapper on the part of the weight vector - * size of cumGrad is based on weightSize provided by - * implementation of LayerModel + * Computes the gradient. + * cumGrad is a wrapper on the part of the weight vector. + * Size of cumGrad is based on weightSize provided by + * implementation of LayerModel. * * @param delta delta for this layer * @param input input data @@ -197,11 +198,11 @@ private[ann] object AffineLayerModel { } /** - * Initialize weights randomly in the interval - * Uses [Bottou-88] heuristic [-a/sqrt(in); a/sqrt(in)] - * where a is chosen in a such way that the weight variance corresponds + * Initialize weights randomly in the interval. + * Uses [Bottou-88] heuristic [-a/sqrt(in); a/sqrt(in)], + * where `a` is chosen in such a way that the weight variance corresponds * to the points to the maximal curvature of the activation function - * (which is approximately 2.38 for a standard sigmoid) + * (which is approximately 2.38 for a standard sigmoid). * * @param numIn number of inputs * @param numOut number of outputs @@ -306,7 +307,7 @@ private[ann] class FunctionalLayer (val activationFunction: ActivationFunction) /** * Functional layer model. Holds no weights. * - * @param layer functiona layer + * @param layer functional layer */ private[ann] class FunctionalLayerModel private[ann] (val layer: FunctionalLayer) extends LayerModel { @@ -352,6 +353,7 @@ private[ann] trait TopologyModel extends Serializable { * Array of layer models */ val layerModels: Array[LayerModel] + /** * Forward propagation * @@ -410,7 +412,7 @@ private[ml] object FeedForwardTopology { * Creates a multi-layer perceptron * * @param layerSizes sizes of layers including input and output size - * @param softmaxOnTop wether to use SoftMax or Sigmoid function for an output layer. + * @param softmaxOnTop whether to use SoftMax or Sigmoid function for an output layer. * Softmax is default * @return multilayer perceptron topology */ |