Merge branch 'master' into issue_103

generated_code_examples/python/classification/decision_tree.py

@@ -1,16 +1,15 @@
-import numpy as np
 def score(input):
     if (input[2]) <= (2.6):
-        var0 = np.asarray([1.0, 0.0, 0.0])
+        var0 = [1.0, 0.0, 0.0]
     else:
         if (input[2]) <= (4.8500004):
             if (input[3]) <= (1.6500001):
-                var0 = np.asarray([0.0, 1.0, 0.0])
+                var0 = [0.0, 1.0, 0.0]
             else:
-                var0 = np.asarray([0.0, 0.3333333333333333, 0.6666666666666666])
+                var0 = [0.0, 0.3333333333333333, 0.6666666666666666]
         else:
             if (input[3]) <= (1.75):
-                var0 = np.asarray([0.0, 0.42857142857142855, 0.5714285714285714])
+                var0 = [0.0, 0.42857142857142855, 0.5714285714285714]
             else:
-                var0 = np.asarray([0.0, 0.0, 1.0])
+                var0 = [0.0, 0.0, 1.0]
     return var0

generated_code_examples/python/classification/lightgbm.py

@@ -1,4 +1,4 @@
-import numpy as np
+import math
 def score(input):
     if (input[2]) > (1.8):
         if (input[2]) > (4.250000000000001):
@@ -14,7 +14,7 @@ def score(input):
             var1 = -0.07237070828653688
     else:
         var1 = 0.12984943093573026
-    var2 = np.exp(((0) + (var0)) + (var1))
+    var2 = math.exp(((0) + (var0)) + (var1))
     if (input[2]) > (1.8):
         if (input[2]) > (4.8500000000000005):
             var3 = -1.1807342692411888
@@ -29,7 +29,7 @@ def score(input):
             var4 = 0.11960489254350348
     else:
         var4 = -0.07151978915296087
-    var5 = np.exp(((0) + (var3)) + (var4))
+    var5 = math.exp(((0) + (var3)) + (var4))
     if (input[2]) > (4.8500000000000005):
         if (input[3]) > (1.9500000000000002):
             var6 = -0.9298942558407184
@@ -50,6 +50,6 @@ def score(input):
             var7 = -0.052710589717642864
         else:
             var7 = -0.07292857712854424
-    var8 = np.exp(((0) + (var6)) + (var7))
+    var8 = math.exp(((0) + (var6)) + (var7))
     var9 = ((var2) + (var5)) + (var8)
-    return np.asarray([(var2) / (var9), (var5) / (var9), (var8) / (var9)])
+    return [(var2) / (var9), (var5) / (var9), (var8) / (var9)]

generated_code_examples/python/classification/linear.py

@@ -1,3 +1,2 @@
-import numpy as np
 def score(input):
-    return np.asarray([((((0.26145874358806076) + ((input[0]) * (0.4247411605356963))) + ((input[1]) * (1.3963906033045022))) + ((input[2]) * (-2.215054318516674))) + ((input[3]) * (-0.9587396176450291)), ((((1.1348839223808753) + ((input[0]) * (0.25679659769994584))) + ((input[1]) * (-1.3904789369835584))) + ((input[2]) * (0.5966830233112762))) + ((input[3]) * (-1.269002272638834)), ((((-1.216280201256021) + ((input[0]) * (-1.635776698917701))) + ((input[1]) * (-1.504063872842283))) + ((input[2]) * (2.4278359331292623))) + ((input[3]) * (2.346931069336734))])
+    return [((((0.2614587435880605) + ((input[0]) * (0.42474116053569605))) + ((input[1]) * (1.3963906033045026))) + ((input[2]) * (-2.215054318516674))) + ((input[3]) * (-0.9587396176450289)), ((((1.1348839223808307) + ((input[0]) * (0.2567965976997648))) + ((input[1]) * (-1.3904789369836008))) + ((input[2]) * (0.596683023311173))) + ((input[3]) * (-1.2690022726388828)), ((((-1.2162802012560197) + ((input[0]) * (-1.6357766989177105))) + ((input[1]) * (-1.5040638728422817))) + ((input[2]) * (2.427835933129272))) + ((input[3]) * (2.3469310693367276))]

generated_code_examples/python/classification/random_forest.py

@@ -1,29 +1,29 @@
 import numpy as np
 def score(input):
     if (input[3]) <= (0.8):
-        var0 = np.asarray([1.0, 0.0, 0.0])
+        var0 = [1.0, 0.0, 0.0]
     else:
         if (input[2]) <= (4.8500004):
-            var0 = np.asarray([0.0, 0.9795918367346939, 0.02040816326530612])
+            var0 = [0.0, 0.9795918367346939, 0.02040816326530612]
         else:
             if (input[3]) <= (1.75):
                 if (input[3]) <= (1.6500001):
-                    var0 = np.asarray([0.0, 0.25, 0.75])
+                    var0 = [0.0, 0.25, 0.75]
                 else:
-                    var0 = np.asarray([0.0, 1.0, 0.0])
+                    var0 = [0.0, 1.0, 0.0]
             else:
-                var0 = np.asarray([0.0, 0.0, 1.0])
+                var0 = [0.0, 0.0, 1.0]
     if (input[3]) <= (0.8):
-        var1 = np.asarray([1.0, 0.0, 0.0])
+        var1 = [1.0, 0.0, 0.0]
     else:
         if (input[0]) <= (6.05):
             if (input[2]) <= (4.9):
-                var1 = np.asarray([0.0, 0.9032258064516129, 0.0967741935483871])
+                var1 = [0.0, 0.9032258064516129, 0.0967741935483871]
             else:
-                var1 = np.asarray([0.0, 0.0, 1.0])
+                var1 = [0.0, 0.0, 1.0]
         else:
             if (input[3]) <= (1.75):
-                var1 = np.asarray([0.0, 0.8, 0.2])
+                var1 = [0.0, 0.8, 0.2]
             else:
-                var1 = np.asarray([0.0, 0.0, 1.0])
-    return ((var0) * (0.5)) + ((var1) * (0.5))
+                var1 = [0.0, 0.0, 1.0]
+    return (np.asarray((np.asarray(var0)) * (0.5))) + (np.asarray((np.asarray(var1)) * (0.5)))