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mandar2812mandar2812
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[dynaml-core]:
 -- Added progress bar -- Removing unnecessary print statements #80
1 parent 67940b9 commit 7f9c577

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9 files changed

+625
-295
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9 files changed

+625
-295
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‎dynaml-core/src/main/scala/io/github/mandar2812/dynaml/kernels/SVMKernel.scala

+28-28
Original file line numberDiff line numberDiff line change
@@ -85,7 +85,7 @@ object SVMKernel {
8585
(i, j) => if (i >= j) kernelIndex((i,j)) else kernelIndex((j,i))
8686
}
8787

88-
println(" Dimensions: " + kernel.rows + " x " + kernel.cols)
88+
//println(" Dimensions: " + kernel.rows + " x " + kernel.cols)
8989
new SVMKernelMatrix(kernel, length)
9090
}
9191

@@ -98,7 +98,7 @@ object SVMKernel {
9898
.map(s => ((s.head._2, s.last._2), eval(s.head._1, s.last._1)))
9999
.toMap
100100

101-
println(" Dimensions: " + data1.length + " x " + data2.length)
101+
//println(" Dimensions: " + data1.length + " x " + data2.length)
102102
DenseMatrix.tabulate[Double](data1.length, data2.length){
103103
(i, j) => kernelIndex((i,j))
104104
}
@@ -112,8 +112,8 @@ object SVMKernel {
112112
Map[String, DenseMatrix[Double]] = {
113113

114114
val (rows, cols) = (data1.length, data1.length)
115-
println("Constructing Kernel/Grad Matrices")
116-
println(" Dimensions: " + rows + " x " + cols)
115+
//println("Constructing Kernel/Grad Matrices")
116+
//println(" Dimensions: " + rows + " x " + cols)
117117

118118
val keys = Seq("kernel-matrix") ++ hyper_parameters
119119

@@ -125,8 +125,8 @@ object SVMKernel {
125125
else (k, ((s.head._2, s.last._2), evalGrad(k)(s.head._1, s.last._1))))
126126
}).groupBy(_._1).map(cl => {
127127

128-
if (cl._1 == "kernel-matrix") println("Constructing Kernel Matrix")
129-
else println("Constructing Grad Matrix for: "+cl._1)
128+
//if (cl._1 == "kernel-matrix") //println("Constructing Kernel Matrix")
129+
//else //println("Constructing Grad Matrix for: "+cl._1)
130130

131131
val kernelIndex = cl._2.map(_._2).toMap
132132

@@ -152,8 +152,8 @@ object SVMKernel {
152152
Map[String, DenseMatrix[Double]] = {
153153

154154
val (rows, cols) = (data1.length, data2.length)
155-
println("Constructing Kernel/Grad Matrices")
156-
println(" Dimensions: " + rows + " x " + cols)
155+
//println("Constructing Kernel/Grad Matrices")
156+
//println(" Dimensions: " + rows + " x " + cols)
157157

158158
val keys = Seq("kernel-matrix") ++ hyper_parameters
159159

@@ -164,8 +164,8 @@ object SVMKernel {
164164
else (k, ((s.head._2, s.last._2), evalGrad(k)(s.head._1, s.last._1))))
165165
}).groupBy(_._1).map(cl => {
166166

167-
if (cl._1 == "kernel-matrix") println("Constructing Kernel Matrix")
168-
else println("Constructing Grad Matrix for: "+cl._1)
167+
//if (cl._1 == "kernel-matrix") //println("Constructing Kernel Matrix")
168+
//else //println("Constructing Grad Matrix for: "+cl._1)
169169

170170
val kernelIndex = cl._2.map(_._2).toMap
171171

@@ -187,25 +187,25 @@ object SVMKernel {
187187

188188
val (rows, cols) = (length, length)
189189

190-
println("Constructing partitioned kernel matrix.")
191-
println("Dimension: " + rows + " x " + cols)
190+
//println("Constructing partitioned kernel matrix.")
191+
//println("Dimension: " + rows + " x " + cols)
192192

193193
val (num_R_blocks, num_C_blocks) = (
194194
math.ceil(rows.toDouble/numElementsPerRowBlock).toLong,
195195
math.ceil(cols.toDouble/numElementsPerColBlock).toLong)
196196

197-
println("Blocks: " + num_R_blocks + " x " + num_C_blocks)
197+
//println("Blocks: " + num_R_blocks + " x " + num_C_blocks)
198198
val partitionedData = data.grouped(numElementsPerRowBlock).zipWithIndex.toStream
199199

200-
println("~~~~~~~~~~~~~~~~~~~~~~~")
201-
println("Constructing Partitions")
200+
//println("~~~~~~~~~~~~~~~~~~~~~~~")
201+
//println("Constructing Partitions")
202202
new PartitionedPSDMatrix(
203203
utils.combine(Seq(partitionedData, partitionedData))
204204
.filter(c => c.head._2 >= c.last._2)
205205
.toStream.map(c => {
206206

207207
val partitionIndex = (c.head._2.toLong, c.last._2.toLong)
208-
println(":- Partition: "+partitionIndex)
208+
//println(":- Partition: "+partitionIndex)
209209

210210
val matrix =
211211
if(partitionIndex._1 == partitionIndex._2)
@@ -226,22 +226,22 @@ object SVMKernel {
226226

227227
val (rows, cols) = (data1.length, data2.length)
228228

229-
println("Constructing cross partitioned kernel matrix.")
230-
println("Dimension: " + rows + " x " + cols)
229+
//println("Constructing cross partitioned kernel matrix.")
230+
//println("Dimension: " + rows + " x " + cols)
231231

232232
val (num_R_blocks, num_C_blocks) = (
233233
math.ceil(rows.toDouble/numElementsPerRowBlock).toLong,
234234
math.ceil(cols.toDouble/numElementsPerColBlock).toLong)
235235

236-
println("Blocks: " + num_R_blocks + " x " + num_C_blocks)
237-
println("~~~~~~~~~~~~~~~~~~~~~~~")
238-
println("Constructing Partitions")
236+
//println("Blocks: " + num_R_blocks + " x " + num_C_blocks)
237+
//println("~~~~~~~~~~~~~~~~~~~~~~~")
238+
//println("Constructing Partitions")
239239
new PartitionedMatrix(utils.combine(Seq(
240240
data1.grouped(numElementsPerRowBlock).zipWithIndex.toStream,
241241
data2.grouped(numElementsPerColBlock).zipWithIndex.toStream)
242242
).toStream.map(c => {
243243
val partitionIndex = (c.head._2.toLong, c.last._2.toLong)
244-
println(":- Partition: "+partitionIndex)
244+
//println(":- Partition: "+partitionIndex)
245245
val matrix = crossKernelMatrix(c.head._1, c.last._1, eval)
246246
(partitionIndex, matrix)
247247
}), rows, cols, num_R_blocks, num_C_blocks)
@@ -258,18 +258,18 @@ object SVMKernel {
258258

259259
val (rows, cols) = (length, length)
260260

261-
println("Constructing partitioned kernel matrix and its derivatives")
262-
println("Dimension: " + rows + " x " + cols)
261+
//println("Constructing partitioned kernel matrix and its derivatives")
262+
//println("Dimension: " + rows + " x " + cols)
263263

264264
val (num_R_blocks, num_C_blocks) = (
265265
math.ceil(rows.toDouble/numElementsPerRowBlock).toLong,
266266
math.ceil(cols.toDouble/numElementsPerColBlock).toLong)
267267

268-
println("Blocks: " + num_R_blocks + " x " + num_C_blocks)
268+
//println("Blocks: " + num_R_blocks + " x " + num_C_blocks)
269269
val partitionedData = data.grouped(numElementsPerRowBlock).zipWithIndex.toStream
270270

271-
println("~~~~~~~~~~~~~~~~~~~~~~~")
272-
println("Constructing Partitions")
271+
//println("~~~~~~~~~~~~~~~~~~~~~~~")
272+
//println("Constructing Partitions")
273273

274274

275275
//Build the result using flatMap - reduce
@@ -278,7 +278,7 @@ object SVMKernel {
278278
.toStream.flatMap(c => {
279279
val partitionIndex = (c.head._2.toLong, c.last._2.toLong)
280280
print("\n")
281-
println(":- Partition: "+partitionIndex)
281+
//println(":- Partition: "+partitionIndex)
282282

283283
if(partitionIndex._1 == partitionIndex._2) {
284284
SVMKernel.buildKernelGradMatrix(

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