rebase fix

artidoro · artidoro · commit 41f062f839aa · 2019-04-16T15:20:24.000-07:00
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LbfgsMaximumEntropy.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LbfgsMaximumEntropy.cs
@@ -59,13 +59,14 @@ public static void Example()
             //  Micro Accuracy: 0.91
             //  Macro Accuracy: 0.91
             //  Log Loss: 0.24
-            //  Log Loss Reduction: 0.78
+            //  Log Loss Reduction: 0.79
         }
 
+        // Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.
         private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)
         {
             var random = new Random(seed);
-            float randomFloat() => (float)random.NextDouble();
+            float randomFloat() => (float)(random.NextDouble() - 0.5);
             for (int i = 0; i < count; i++)
             {
                 // Generate Labels that are integers 1, 2 or 3
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LbfgsMaximumEntropy.tt b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LbfgsMaximumEntropy.tt
@@ -8,6 +8,7 @@ string TrainerOptions = null;
 string OptionsInclude = "";
 string Comments = "";
 bool CacheData = false;
+string DataGenerationComments= "// Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.";
 
 string ExpectedOutputPerInstance = @"// Expected output:
             //   Label: 1, Prediction: 1
@@ -20,5 +21,5 @@ string ExpectedOutput = @"// Expected output:
             //  Micro Accuracy: 0.91
             //  Macro Accuracy: 0.91
             //  Log Loss: 0.24
-            //  Log Loss Reduction: 0.78";
+            //  Log Loss Reduction: 0.79";
 #>
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LbfgsMaximumEntropyWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LbfgsMaximumEntropyWithOptions.cs
@@ -72,10 +72,11 @@ public static void Example()
             //  Log Loss Reduction: 0.80
         }
 
+        // Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.
         private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)
         {
             var random = new Random(seed);
-            float randomFloat() => (float)random.NextDouble();
+            float randomFloat() => (float)(random.NextDouble() - 0.5);
             for (int i = 0; i < count; i++)
             {
                 // Generate Labels that are integers 1, 2 or 3
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LbfgsMaximumEntropyWithOptions.tt b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LbfgsMaximumEntropyWithOptions.tt
@@ -12,7 +12,7 @@ string TrainerOptions = @"LbfgsMaximumEntropyMulticlassTrainer.Options
 
 string OptionsInclude = "using Microsoft.ML.Trainers;";
 string Comments = "";
-
+string DataGenerationComments= "// Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.";
 bool CacheData = false;
 
 string ExpectedOutputPerInstance = @"// Expected output:
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LightGbm.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LightGbm.cs
@@ -61,13 +61,14 @@ public static void Example()
             //  Micro Accuracy: 0.99
             //  Macro Accuracy: 0.99
             //  Log Loss: 0.05
-            //  Log Loss Reduction: 0.96
+            //  Log Loss Reduction: 0.95
         }
 
+        // Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.
         private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)
         {
             var random = new Random(seed);
-            float randomFloat() => (float)random.NextDouble();
+            float randomFloat() => (float)(random.NextDouble() - 0.5);
             for (int i = 0; i < count; i++)
             {
                 // Generate Labels that are integers 1, 2 or 3
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LightGbm.tt b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LightGbm.tt
@@ -10,6 +10,7 @@ string Comments = @"
         // This example requires installation of additional NuGet package
         // <a href=""https://www.nuget.org/packages/Microsoft.ML.FastTree/"">Microsoft.ML.FastTree</a>.";
 bool CacheData = false;
+string DataGenerationComments= "// Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.";
 
 string ExpectedOutputPerInstance = @"// Expected output:
             //   Label: 1, Prediction: 1
@@ -22,5 +23,5 @@ string ExpectedOutput = @"// Expected output:
             //  Micro Accuracy: 0.99
             //  Macro Accuracy: 0.99
             //  Log Loss: 0.05
-            //  Log Loss Reduction: 0.96";
+            //  Log Loss Reduction: 0.95";
 #>
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LightGbmWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LightGbmWithOptions.cs
@@ -72,14 +72,15 @@ public static void Example()
             // Expected output:
             //  Micro Accuracy: 0.98
             //  Macro Accuracy: 0.98
-            //  Log Loss: 0.06
-            //  Log Loss Reduction: 0.80
+            //  Log Loss: 0.07
+            //  Log Loss Reduction: 0.94
         }
 
+        // Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.
         private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)
         {
             var random = new Random(seed);
-            float randomFloat() => (float)random.NextDouble();
+            float randomFloat() => (float)(random.NextDouble() - 0.5);
             for (int i = 0; i < count; i++)
             {
                 // Generate Labels that are integers 1, 2 or 3
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LightGbmWithOptions.tt b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LightGbmWithOptions.tt
@@ -18,6 +18,7 @@ string Comments = @"
         // <a href=""https://www.nuget.org/packages/Microsoft.ML.FastTree/"">Microsoft.ML.FastTree</a>.";
 
 bool CacheData = false;
+string DataGenerationComments= "// Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.";
 
 string ExpectedOutputPerInstance = @"// Expected output:
             //   Label: 1, Prediction: 1
@@ -29,6 +30,6 @@ string ExpectedOutputPerInstance = @"// Expected output:
 string ExpectedOutput = @"// Expected output:
             //  Micro Accuracy: 0.98
             //  Macro Accuracy: 0.98
-            //  Log Loss: 0.06
-            //  Log Loss Reduction: 0.80";
+            //  Log Loss: 0.07
+            //  Log Loss Reduction: 0.94";
 #>
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/OneVersusAll.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/OneVersusAll.cs
@@ -58,11 +58,10 @@ public static void Example()
             // Expected output:
             //  Micro Accuracy: 0.90
             //  Macro Accuracy: 0.90
-            //  Log Loss: 0.37
-            //  Log Loss Reduction: 0.67
+            //  Log Loss: 0.36
+            //  Log Loss Reduction: 0.68
         }
 
-        
         // Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.
         private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)
         {
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/OneVersusAll.tt b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/OneVersusAll.tt
@@ -7,8 +7,8 @@ string TrainerOptions = null;
 
 string OptionsInclude = "";
 string Comments= "";
-string DataGenerationComments= @"
-        // Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.";
+string DataGenerationComments= "// Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.";
+
 bool CacheData = false;
 
 string ExpectedOutputPerInstance= @"// Expected output:
@@ -21,6 +21,6 @@ string ExpectedOutputPerInstance= @"// Expected output:
 string ExpectedOutput = @"// Expected output:
             //  Micro Accuracy: 0.90
             //  Macro Accuracy: 0.90
-            //  Log Loss: 0.37
-            //  Log Loss Reduction: 0.67";
+            //  Log Loss: 0.36
+            //  Log Loss Reduction: 0.68";
 #>
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/PairwiseCoupling.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/PairwiseCoupling.cs
@@ -58,11 +58,10 @@ public static void Example()
             // Expected output:
             //  Micro Accuracy: 0.90
             //  Macro Accuracy: 0.90
-            //  Log Loss: 0.38
-            //  Log Loss Reduction: 0.66
+            //  Log Loss: 0.36
+            //  Log Loss Reduction: 0.67
         }
 
-        
         // Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.
         private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)
         {
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/PairwiseCoupling.tt b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/PairwiseCoupling.tt
@@ -7,8 +7,7 @@ string TrainerOptions = null;
 
 string OptionsInclude = "";
 string Comments= "";
-string DataGenerationComments= @"
-        // Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.";
+string DataGenerationComments= "// Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.";
 bool CacheData = false;
 
 string ExpectedOutputPerInstance= @"// Expected output:
@@ -21,6 +20,6 @@ string ExpectedOutputPerInstance= @"// Expected output:
 string ExpectedOutput = @"// Expected output:
             //  Micro Accuracy: 0.90
             //  Macro Accuracy: 0.90
-            //  Log Loss: 0.38
-            //  Log Loss Reduction: 0.66";
+            //  Log Loss: 0.36
+            //  Log Loss Reduction: 0.67";
 #>
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaMaximumEntropy.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaMaximumEntropy.cs
@@ -68,10 +68,11 @@ public static void Example()
             //  Log Loss Reduction: 0.80
         }
 
+        // Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.
         private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)
         {
             var random = new Random(seed);
-            float randomFloat() => (float)random.NextDouble();
+            float randomFloat() => (float)(random.NextDouble() - 0.5);
             for (int i = 0; i < count; i++)
             {
                 // Generate Labels that are integers 1, 2 or 3
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaMaximumEntropy.tt b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaMaximumEntropy.tt
@@ -8,6 +8,7 @@ string TrainerOptions = null;
 string OptionsInclude = "";
 string Comments = "";
 bool CacheData = true;
+string DataGenerationComments= "// Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.";
 
 string ExpectedOutputPerInstance = @"// Expected output:
             //   Label: 1, Prediction: 1
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaMaximumEntropyWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaMaximumEntropyWithOptions.cs
@@ -73,16 +73,17 @@ public static void Example()
             PrintMetrics(metrics);
             
             // Expected output:
-            //  Micro Accuracy: 0.91
-            //  Macro Accuracy: 0.91
-            //  Log Loss: 0.46
-            //  Log Loss Reduction: 0.58
+            //  Micro Accuracy: 0.92
+            //  Macro Accuracy: 0.92
+            //  Log Loss: 0.31
+            //  Log Loss Reduction: 0.72
         }
 
+        // Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.
         private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)
         {
             var random = new Random(seed);
-            float randomFloat() => (float)random.NextDouble();
+            float randomFloat() => (float)(random.NextDouble() - 0.5);
             for (int i = 0; i < count; i++)
             {
                 // Generate Labels that are integers 1, 2 or 3
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaMaximumEntropyWithOptions.tt b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaMaximumEntropyWithOptions.tt
@@ -14,6 +14,8 @@ string TrainerOptions = @"SdcaMaximumEntropyMulticlassTrainer.Options
 string OptionsInclude = "using Microsoft.ML.Trainers;";
 string Comments = "";
 bool CacheData = true;
+string DataGenerationComments= "// Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.";
+
 
 string ExpectedOutputPerInstance = @"// Expected output:
             //   Label: 1, Prediction: 1
@@ -23,8 +25,8 @@ string ExpectedOutputPerInstance = @"// Expected output:
             //   Label: 3, Prediction: 3";
 
 string ExpectedOutput = @"// Expected output:
-            //  Micro Accuracy: 0.91
-            //  Macro Accuracy: 0.91
-            //  Log Loss: 0.46
-            //  Log Loss Reduction: 0.58";
+            //  Micro Accuracy: 0.92
+            //  Macro Accuracy: 0.92
+            //  Log Loss: 0.31
+            //  Log Loss Reduction: 0.72";
 #>
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaNonCalibrated.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaNonCalibrated.cs
@@ -64,14 +64,15 @@ public static void Example()
             // Expected output:
             //  Micro Accuracy: 0.91
             //  Macro Accuracy: 0.91
-            //  Log Loss: 0.00
-            //  Log Loss Reduction: 1.00
+            //  Log Loss: 0.57
+            //  Log Loss Reduction: 0.48
         }
 
+        // Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.
         private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)
         {
             var random = new Random(seed);
-            float randomFloat() => (float)random.NextDouble();
+            float randomFloat() => (float)(random.NextDouble() - 0.5);
             for (int i = 0; i < count; i++)
             {
                 // Generate Labels that are integers 1, 2 or 3
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaNonCalibrated.tt b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaNonCalibrated.tt
@@ -8,6 +8,7 @@ string TrainerOptions = null;
 string OptionsInclude = "";
 string Comments = "";
 bool CacheData = true;
+string DataGenerationComments= "// Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.";
 
 string ExpectedOutputPerInstance = @"// Expected output:
             //   Label: 1, Prediction: 1
@@ -19,6 +20,6 @@ string ExpectedOutputPerInstance = @"// Expected output:
 string ExpectedOutput = @"// Expected output:
             //  Micro Accuracy: 0.91
             //  Macro Accuracy: 0.91
-            //  Log Loss: 0.00
-            //  Log Loss Reduction: 1.00";
+            //  Log Loss: 0.57
+            //  Log Loss Reduction: 0.48";
 #>
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaNonCalibratedWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaNonCalibratedWithOptions.cs
@@ -75,14 +75,15 @@ public static void Example()
             // Expected output:
             //  Micro Accuracy: 0.91
             //  Macro Accuracy: 0.91
-            //  Log Loss: 0.36
-            //  Log Loss Reduction: 0.67
+            //  Log Loss: 0.22
+            //  Log Loss Reduction: 0.80
         }
 
+        // Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.
         private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)
         {
             var random = new Random(seed);
-            float randomFloat() => (float)random.NextDouble();
+            float randomFloat() => (float)(random.NextDouble() - 0.5);
             for (int i = 0; i < count; i++)
             {
                 // Generate Labels that are integers 1, 2 or 3
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaNonCalibratedWithOptions.tt b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaNonCalibratedWithOptions.tt
@@ -14,6 +14,7 @@ string TrainerOptions = @"SdcaNonCalibratedMulticlassTrainer.Options
 string OptionsInclude = "using Microsoft.ML.Trainers;";
 string Comments = "";
 bool CacheData = true;
+string DataGenerationComments= "// Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.";
 
 string ExpectedOutputPerInstance = @"// Expected output:
             //   Label: 1, Prediction: 1
@@ -25,6 +26,6 @@ string ExpectedOutputPerInstance = @"// Expected output:
 string ExpectedOutput = @"// Expected output:
             //  Micro Accuracy: 0.91
             //  Macro Accuracy: 0.91
-            //  Log Loss: 0.36
-            //  Log Loss Reduction: 0.67";
+            //  Log Loss: 0.22
+            //  Log Loss Reduction: 0.80";
 #>

Original file line number	Diff line number	Diff line change
`@@ -59,13 +59,14 @@ public static void Example()`
`59`	`59`	`// Micro Accuracy: 0.91`
`60`	`60`	`// Macro Accuracy: 0.91`
`61`	`61`	`// Log Loss: 0.24`
`62`		`- // Log Loss Reduction: 0.78`
	`62`	`+ // Log Loss Reduction: 0.79`
`63`	`63`	`}`
`64`	`64`
	`65`	`+ // Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.`
`65`	`66`	`private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)`
`66`	`67`	`{`
`67`	`68`	`var random = new Random(seed);`
`68`		`- float randomFloat() => (float)random.NextDouble();`
	`69`	`+ float randomFloat() => (float)(random.NextDouble() - 0.5);`
`69`	`70`	`for (int i = 0; i < count; i++)`
`70`	`71`	`{`
`71`	`72`	`// Generate Labels that are integers 1, 2 or 3`
Original file line number	Diff line number	Diff line change
`@@ -72,10 +72,11 @@ public static void Example()`
`72`	`72`	`// Log Loss Reduction: 0.80`
`73`	`73`	`}`
`74`	`74`
	`75`	`+ // Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.`
`75`	`76`	`private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)`
`76`	`77`	`{`
`77`	`78`	`var random = new Random(seed);`
`78`		`- float randomFloat() => (float)random.NextDouble();`
	`79`	`+ float randomFloat() => (float)(random.NextDouble() - 0.5);`
`79`	`80`	`for (int i = 0; i < count; i++)`
`80`	`81`	`{`
`81`	`82`	`// Generate Labels that are integers 1, 2 or 3`
Original file line number	Diff line number	Diff line change
`@@ -61,13 +61,14 @@ public static void Example()`
`61`	`61`	`// Micro Accuracy: 0.99`
`62`	`62`	`// Macro Accuracy: 0.99`
`63`	`63`	`// Log Loss: 0.05`
`64`		`- // Log Loss Reduction: 0.96`
	`64`	`+ // Log Loss Reduction: 0.95`
`65`	`65`	`}`
`66`	`66`
	`67`	`+ // Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.`
`67`	`68`	`private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)`
`68`	`69`	`{`
`69`	`70`	`var random = new Random(seed);`
`70`		`- float randomFloat() => (float)random.NextDouble();`
	`71`	`+ float randomFloat() => (float)(random.NextDouble() - 0.5);`
`71`	`72`	`for (int i = 0; i < count; i++)`
`72`	`73`	`{`
`73`	`74`	`// Generate Labels that are integers 1, 2 or 3`
Original file line number	Diff line number	Diff line change
`@@ -72,14 +72,15 @@ public static void Example()`
`72`	`72`	`// Expected output:`
`73`	`73`	`// Micro Accuracy: 0.98`
`74`	`74`	`// Macro Accuracy: 0.98`
`75`		`- // Log Loss: 0.06`
`76`		`- // Log Loss Reduction: 0.80`
	`75`	`+ // Log Loss: 0.07`
	`76`	`+ // Log Loss Reduction: 0.94`
`77`	`77`	`}`
`78`	`78`
	`79`	`+ // Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.`
`79`	`80`	`private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)`
`80`	`81`	`{`
`81`	`82`	`var random = new Random(seed);`
`82`		`- float randomFloat() => (float)random.NextDouble();`
	`83`	`+ float randomFloat() => (float)(random.NextDouble() - 0.5);`
`83`	`84`	`for (int i = 0; i < count; i++)`
`84`	`85`	`{`
`85`	`86`	`// Generate Labels that are integers 1, 2 or 3`
Original file line number	Diff line number	Diff line change
`@@ -58,11 +58,10 @@ public static void Example()`
`58`	`58`	`// Expected output:`
`59`	`59`	`// Micro Accuracy: 0.90`
`60`	`60`	`// Macro Accuracy: 0.90`
`61`		`- // Log Loss: 0.37`
`62`		`- // Log Loss Reduction: 0.67`
	`61`	`+ // Log Loss: 0.36`
	`62`	`+ // Log Loss Reduction: 0.68`
`63`	`63`	`}`
`64`	`64`
`65`		`-`
`66`	`65`	`// Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.`
`67`	`66`	`private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)`
`68`	`67`	`{`
Original file line number	Diff line number	Diff line change
`@@ -68,10 +68,11 @@ public static void Example()`
`68`	`68`	`// Log Loss Reduction: 0.80`
`69`	`69`	`}`
`70`	`70`
	`71`	`+ // Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.`
`71`	`72`	`private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)`
`72`	`73`	`{`
`73`	`74`	`var random = new Random(seed);`
`74`		`- float randomFloat() => (float)random.NextDouble();`
	`75`	`+ float randomFloat() => (float)(random.NextDouble() - 0.5);`
`75`	`76`	`for (int i = 0; i < count; i++)`
`76`	`77`	`{`
`77`	`78`	`// Generate Labels that are integers 1, 2 or 3`
Original file line number	Diff line number	Diff line change
`@@ -64,14 +64,15 @@ public static void Example()`
`64`	`64`	`// Expected output:`
`65`	`65`	`// Micro Accuracy: 0.91`
`66`	`66`	`// Macro Accuracy: 0.91`
`67`		`- // Log Loss: 0.00`
`68`		`- // Log Loss Reduction: 1.00`
	`67`	`+ // Log Loss: 0.57`
	`68`	`+ // Log Loss Reduction: 0.48`
`69`	`69`	`}`
`70`	`70`
	`71`	`+ // Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.`
`71`	`72`	`private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)`
`72`	`73`	`{`
`73`	`74`	`var random = new Random(seed);`
`74`		`- float randomFloat() => (float)random.NextDouble();`
	`75`	`+ float randomFloat() => (float)(random.NextDouble() - 0.5);`
`75`	`76`	`for (int i = 0; i < count; i++)`
`76`	`77`	`{`
`77`	`78`	`// Generate Labels that are integers 1, 2 or 3`
Original file line number	Diff line number	Diff line change
`@@ -75,14 +75,15 @@ public static void Example()`
`75`	`75`	`// Expected output:`
`76`	`76`	`// Micro Accuracy: 0.91`
`77`	`77`	`// Macro Accuracy: 0.91`
`78`		`- // Log Loss: 0.36`
`79`		`- // Log Loss Reduction: 0.67`
	`78`	`+ // Log Loss: 0.22`
	`79`	`+ // Log Loss Reduction: 0.80`
`80`	`80`	`}`
`81`	`81`
	`82`	`+ // Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.`
`82`	`83`	`private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)`
`83`	`84`	`{`
`84`	`85`	`var random = new Random(seed);`
`85`		`- float randomFloat() => (float)random.NextDouble();`
	`86`	`+ float randomFloat() => (float)(random.NextDouble() - 0.5);`
`86`	`87`	`for (int i = 0; i < count; i++)`
`87`	`88`	`{`
`88`	`89`	`// Generate Labels that are integers 1, 2 or 3`