diff --git a/test/pred-sets/ref/cats.predict b/test/pred-sets/ref/cats.predict index df1a5c18b8f..09d464d5866 100644 --- a/test/pred-sets/ref/cats.predict +++ b/test/pred-sets/ref/cats.predict @@ -1,57 +1,57 @@ -17118.012,0.00016043648 -15724.644,0.00016043648 -15331.798,0.00016043648 -15138.831,0.00016043648 -13393.029,0.00016043648 -12710.978,0.00016043648 -14503.708,0.00016043648 -15936.4,0.00016043648 -17257.076,0.00016043648 -17208.654,0.00016043648 -14921.187,0.00016043648 -16660.594,0.00016043648 -13486.455,0.00016043648 -6641.8545,2.1031378e-06 -14801.345,0.00016043648 -17399.625,0.00016043648 -16820.953,0.00016043648 -13160.789,0.00016043648 -15094.871,0.00016043648 -13487.722,0.00016043648 -17514.398,0.00016043648 -12287.391,0.00016043648 -17394.318,0.00016043648 -12249.621,0.00016043648 -12830.798,0.00016043648 -14759.012,0.00016043648 -17850.668,0.00016043648 -15202.806,0.00016043648 -16561.379,0.00016043648 -12855.774,0.00016043648 -13520.215,0.00016043648 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+17372.887,0.00016043647 +15202.805,0.00016043647 +17974.736,0.00016043647 diff --git a/test/pred-sets/ref/cats_load.predict b/test/pred-sets/ref/cats_load.predict index 27c5e3b9f13..1402f6525d5 100644 --- a/test/pred-sets/ref/cats_load.predict +++ b/test/pred-sets/ref/cats_load.predict @@ -1,10 +1,10 @@ -2.3137953,0.40625 -1.8475317,0.40625 -2.1299162,0.40625 -1.7430687,0.40625 -14.327164,0.00625 -1.7164373,0.40625 -1.748255,0.40625 -30.00431,0.00625 -0.51225615,0.40625 -2.3816023,0.40625 +25.271912,0.00625 +2.1290207,0.40625 +4.473629,0.00625 +18.205719,0.00625 +2.4059067,0.40625 +9.585819,0.00625 +0.93930376,0.40625 +1.7801242,0.40625 +2.0926502,0.40625 +0.8038121,0.40625 diff --git a/test/pred-sets/ref/cats_room_temp.predict b/test/pred-sets/ref/cats_room_temp.predict index b0c0474ee4c..6fd96ab4646 100644 --- a/test/pred-sets/ref/cats_room_temp.predict +++ b/test/pred-sets/ref/cats_room_temp.predict @@ -1,100 +1,100 @@ -84.607056,0.005 -3.9181187,0.08833334 -39.260834,0.005 -17.99776,0.08833334 -39.331844,0.005 -6.7627044,0.005 -77.12543,0.005 -69.8972,0.005 -17.184662,0.08833334 -54.76903,0.005 -0.66596967,0.005 -13.026539,0.005 -52.522453,0.08833334 -86.18144,0.005 -49.648746,0.08833334 -53.096367,0.08833334 -41.92059,0.005 -88.03693,0.005 -50.15336,0.08833334 -1.6463974,0.005 -57.090523,0.08833334 -25.416866,0.005 -94.471146,0.005 -43.387676,0.005 -59.151047,0.08833334 -38.3481,0.005 -51.486103,0.08833334 -50.88297,0.08833334 -85.89383,0.005 -55.38167,0.005 -51.260296,0.08833334 -85.17497,0.005 -50.680935,0.08833334 -49.601463,0.08833334 -59.61712,0.005 -76.559,0.005 -51.651062,0.08833334 -62.96363,0.005 -7.8253226,0.005 -32.741436,0.005 -21.816528,0.005 -53.33639,0.08833334 -33.895855,0.005 -48.782,0.08833334 -48.93803,0.08833334 -48.645847,0.08833334 -53.199036,0.08833334 -77.73645,0.005 -62.783302,0.005 -18.494978,0.005 -66.79538,0.005 -53.29303,0.08833334 -53.5938,0.08833334 -47.533524,0.005 -8.389192,0.005 -28.030102,0.005 -52.605915,0.08833334 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+66.171425,0.005 +51.027653,0.08833334 +53.797455,0.08833334 +91.31642,0.005 +75.98573,0.005 +51.8337,0.08833334 +95.297424,0.005 +54.301224,0.08833334 +49.036102,0.08833334 +31.043774,0.005 +56.56052,0.005 +48.77767,0.08833334 +53.00767,0.08833334 +49.76527,0.08833334 +28.06632,0.005 +53.554474,0.08833334 +54.24915,0.08833334 +15.149416,0.005 +48.76757,0.08833334 +48.71854,0.08833334 +52.769234,0.08833334 +79.30612,0.005 +2.7464993,0.005 +53.082054,0.08833334 +58.80374,0.005 +49.790363,0.08833334 +56.0045,0.005 +88.90586,0.005 +52.904503,0.08833334 +52.2649,0.08833334 +11.048922,0.005 +53.87539,0.08833334 +49.905197,0.08833334 +42.230206,0.005 +42.714077,0.005 +68.1033,0.005 diff --git a/test/train-sets/ref/cats-predict.stderr b/test/train-sets/ref/cats-predict.stderr index f8c61ac13f1..1f53249872a 100644 --- a/test/train-sets/ref/cats-predict.stderr +++ b/test/train-sets/ref/cats-predict.stderr @@ -15,17 +15,17 @@ Input label = CONTINUOUS Output pred = ACTION_PDF_VALUE average since example example current current current loss last counter weight label predict features -0.000000 0.000000 1 1.0 {185.12,0.6... 17118.01,0 10 -0.000000 0.000000 2 2.0 {772.59,0.4... 15724.64,0 10 -0.226921 0.453841 4 4.0 {14122,0.02,0} 15138.83,0 10 -0.321934 0.416948 8 8.0 {12715.1,0.... 15936.4,0 10 -0.177189 0.032443 16 16.0 {669.12,0.4... 17399.62,0 10 -0.189219 0.201250 32 32.0 {10786.7,0.... 14063.97,0 10 +0.000000 0.000000 1 1.0 {185.12,0.6... 16793.77,0 10 +0.000000 0.000000 2 2.0 {772.59,0.4... 17118.01,0 10 +0.226921 0.453841 4 4.0 {14122,0.02,0} 15724.64,0 10 +0.321934 0.416948 8 8.0 {12715.1,0.... 15138.83,0 10 +0.200445 0.078955 16 16.0 {669.12,0.4... 15936.4,0 10 +0.191026 0.181608 32 32.0 {10786.7,0.... 17399.62,0 10 finished run number of examples = 57 weighted example sum = 57.000000 weighted label sum = 57.000000 -average loss = 0.168316 +average loss = 0.184755 total feature number = 570 Learn() count per node: id=0, #l=17; id=1, #l=0; id=2, #l=0; diff --git a/test/train-sets/ref/cats_load.stderr b/test/train-sets/ref/cats_load.stderr index eef510531fb..4080728be24 100644 --- a/test/train-sets/ref/cats_load.stderr +++ b/test/train-sets/ref/cats_load.stderr @@ -16,10 +16,10 @@ Input label = CONTINUOUS Output pred = ACTION_PDF_VALUE average since example example current current current loss last counter weight label predict features -0.000000 0.000000 1 1.0 {0,0,0.01} 2.31,0.41 6 -0.000000 0.000000 2 2.0 {0.58,0,0.41} 1.85,0.41 6 -0.000000 0.000000 4 4.0 {10.35,0,0.01} 1.74,0.41 6 -0.000000 0.000000 8 8.0 {2.41,0,0.41} 30,0.01 6 +0.000000 0.000000 1 1.0 {0,0,0.01} 25.27,0.01 6 +0.000000 0.000000 2 2.0 {0.58,0,0.41} 2.13,0.41 6 +0.000000 0.000000 4 4.0 {10.35,0,0.01} 18.21,0.01 6 +0.000000 0.000000 8 8.0 {2.41,0,0.41} 1.78,0.41 6 finished run number of examples = 10 diff --git a/test/train-sets/ref/cats_room_temp_pred.stderr b/test/train-sets/ref/cats_room_temp_pred.stderr index 93262b1c2d2..8cc65301a19 100644 --- a/test/train-sets/ref/cats_room_temp_pred.stderr +++ b/test/train-sets/ref/cats_room_temp_pred.stderr @@ -15,18 +15,18 @@ Input label = CONTINUOUS Output pred = ACTION_PDF_VALUE average since example example current current current loss last counter weight label predict features -0.000000 0.000000 1 1.0 {0,25,0} 84.61,0 3 -26.04380 52.08761 2 2.0 {4.07,21.1,... 3.92,0.09 3 -13.02190 0.000000 4 4.0 {72.94,5.26,0} 18,0.09 3 -6.510952 0.000000 8 8.0 {67.13,2.93... 69.9,0 3 -31.39586 56.28078 16 16.0 {6.01,19.35,0} 53.1,0.09 3 -15.71233 0.028805 32 32.0 {59.57,0.92... 85.17,0 3 -11.42273 7.133129 64 64.0 {23.4,7.08,0} 88.78,0 3 +0.000000 0.000000 1 1.0 {0,25,0} 53.2,0.09 3 +0.000000 0.000000 2 2.0 {4.07,21.1,... 9.2,0 3 +0.000000 0.000000 4 4.0 {72.94,5.26,0} 77.29,0 3 +72.07164 144.1432 8 8.0 {67.13,2.93... 19.24,0.09 3 +39.93551 7.799388 16 16.0 {6.01,19.35,0} 67.81,0 3 +20.00477 0.074031 32 32.0 {59.57,0.92... 72.27,0 3 +17.12056 14.23634 64 64.0 {23.4,7.08,0} 30.24,0 3 finished run number of examples = 100 weighted example sum = 100.000000 weighted label sum = 100.000000 -average loss = 8.189957 +average loss = 11.096396 total feature number = 300 Learn() count per node: id=0, #l=32; id=1, #l=18; id=2, #l=44; id=3, #l=10; id=4, #l=20; id=5, #l=48; id=6, #l=12; id=7, #l=8; id=8, #l=6; id=9, #l=7; id=10, #l=18; id=11, #l=28; id=12, #l=9; id=13, #l=9; id=14, #l=3; id=15, #l=0;