Enables Calibrators Tests for Linear Svm (dotnet#233)

* Test Enabled, Debug and Release baseline files added

Author: Anipik

ZBaselines/SingleDebug/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration-out.txt

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+maml.exe CV tr=LinearSVM{iter=100 lambda=0.03} threads=- cali=PAV dout=%Output% data=%Data% seed=1
+Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
+Warning: Skipped 800 instances with missing features during training (over 100 iterations; 8 inst/iter)
+Training calibrator.
+PAV calibrator:  piecewise function approximation has 6 components.
+Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
+Warning: Skipped 800 instances with missing features during training (over 100 iterations; 8 inst/iter)
+Training calibrator.
+PAV calibrator:  piecewise function approximation has 6 components.
+Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
+TEST POSITIVE RATIO:	0.3785 (134.0/(134.0+220.0))
+Confusion table
+          ||======================
+PREDICTED || positive | negative | Recall
+TRUTH     ||======================
+ positive ||      128 |        6 | 0.9552
+ negative ||        7 |      213 | 0.9682
+          ||======================
+Precision ||   0.9481 |   0.9726 |
+OVERALL 0/1 ACCURACY: 0.963277
+LOG LOSS/instance:  Infinity
+Test-set entropy (prior Log-Loss/instance): 0.956998
+LOG-LOSS REDUCTION (RIG): -Infinity
+AUC:                0.994233
+Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
+TEST POSITIVE RATIO:	0.3191 (105.0/(105.0+224.0))
+Confusion table
+          ||======================
+PREDICTED || positive | negative | Recall
+TRUTH     ||======================
+ positive ||       97 |        8 | 0.9238
+ negative ||        2 |      222 | 0.9911
+          ||======================
+Precision ||   0.9798 |   0.9652 |
+OVERALL 0/1 ACCURACY: 0.969605
+LOG LOSS/instance:  0.220291
+Test-set entropy (prior Log-Loss/instance): 0.903454
+LOG-LOSS REDUCTION (RIG): 75.616820
+AUC:                0.997491
+
+OVERALL RESULTS
+---------------------------------------
+AUC:                0.995862 (0.0016)
+Accuracy:           0.966441 (0.0032)
+Positive precision: 0.963973 (0.0158)
+Positive recall:    0.939517 (0.0157)
+Negative precision: 0.968910 (0.0037)
+Negative recall:    0.979627 (0.0114)
+Log-loss:           Infinity (NaN)
+Log-loss reduction: -Infinity (NaN)
+F1 Score:           0.951327 (0.0003)
+AUPRC:              0.991949 (0.0025)
+
+---------------------------------------
+Physical memory usage(MB): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime%	 Time elapsed(s): %Number%
+

ZBaselines/SingleDebug/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration-rp.txt

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+LinearSVM
+AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	/lambda	/iter	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
+0.995862	0.966441	0.963973	0.939517	0.96891	0.979627	Infinity	-Infinity	0.951327	0.991949	0.03	100	LinearSVM	%Data%		%Output%	99	0	0	maml.exe CV tr=LinearSVM{iter=100 lambda=0.03} threads=- cali=PAV dout=%Output% data=%Data% seed=1	/lambda:0.03;/iter:100	
+