From 043648e150a48bfa70c0b45b371cd2e4e66c45cf Mon Sep 17 00:00:00 2001 From: Jack Gerrits Date: Tue, 15 Aug 2023 16:37:53 -0400 Subject: [PATCH] update tests --- test/pred-sets/ref/cats_save.predict | 10 +++++----- test/train-sets/ref/0001-replay.stderr | 12 ++++++------ test/train-sets/ref/cats-train.stderr | 14 +++++++------- test/train-sets/ref/cats_room_temp.stderr | 16 ++++++++-------- test/train-sets/ref/cats_save.stderr | 8 ++++---- 5 files changed, 30 insertions(+), 30 deletions(-) diff --git a/test/pred-sets/ref/cats_save.predict b/test/pred-sets/ref/cats_save.predict index c1e7939b11b..c2113a7759b 100644 --- a/test/pred-sets/ref/cats_save.predict +++ b/test/pred-sets/ref/cats_save.predict @@ -1,10 +1,10 @@ +0.0012017498,0.00625 0.6113522,0.40625 +0.5820448,0.40625 2.2343192,0.40625 +20.461426,0.00625 1.2286053,0.40625 +10.350056,0.00625 22.592487,0.00625 +2.2877245,0.40625 1.3163464,0.40625 -2.1290207,0.40625 -18.205719,0.00625 -9.585819,0.00625 -1.7801242,0.40625 -0.8038121,0.40625 diff --git a/test/train-sets/ref/0001-replay.stderr b/test/train-sets/ref/0001-replay.stderr index c666aba5bec..fda47007a9c 100644 --- a/test/train-sets/ref/0001-replay.stderr +++ b/test/train-sets/ref/0001-replay.stderr @@ -18,18 +18,18 @@ loss last counter weight label predict feat 0.250000 0.250000 8 8.0 0.0000 0.0000 860 0.312500 0.375000 16 16.0 1.0000 0.0000 128 0.343750 0.375000 32 32.0 0.0000 0.0000 176 -0.350946 0.358141 64 64.0 0.0000 0.0275 350 -0.371181 0.391417 128 128.0 1.0000 0.1510 620 -0.292717 0.214252 256 256.0 0.0000 0.3660 410 -0.149202 0.005688 512 512.0 0.0000 0.0000 278 -0.074603 0.000004 1024 1024.0 1.0000 1.0000 170 +0.359375 0.375000 64 64.0 0.0000 0.0275 350 +0.414062 0.468750 128 128.0 1.0000 0.1510 620 +0.433594 0.453125 256 256.0 0.0000 0.3660 410 +0.441406 0.449219 512 512.0 0.0000 0.0000 278 +0.453125 0.464844 1024 1024.0 1.0000 1.0000 170 finished run number of examples per pass = 200 passes used = 8 weighted example sum = 1600.000000 weighted label sum = 728.000000 -average loss = 0.047746 +average loss = 0.455000 best constant = 0.455000 best constant's loss = 0.247975 total feature number = 717536 diff --git a/test/train-sets/ref/cats-train.stderr b/test/train-sets/ref/cats-train.stderr index 0c9cd87a436..ca7a52ba115 100644 --- a/test/train-sets/ref/cats-train.stderr +++ b/test/train-sets/ref/cats-train.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... 6324.15,0 10 -0.000000 0.000000 2 2.0 {772.59,0.4... 16299.34,0 10 -0.226921 0.453841 4 4.0 {14122,0.02,0} 17754.81,0 10 -0.321934 0.416948 8 8.0 {12715.1,0.... 17248.12,0 10 -0.200445 0.078955 16 16.0 {669.12,0.4... 14987.98,0 10 -0.165127 0.129809 32 32.0 {10786.7,0.... 13866.5,0 10 +1.774796 1.774796 1 1.0 {185.12,0.6... 188.57,0 10 +0.887398 0.000000 2 2.0 {772.59,0.4... 12189.73,0 10 +0.670619 0.453841 4 4.0 {14122,0.02,0} 16299.34,0 10 +0.543784 0.416948 8 8.0 {12715.1,0.... 17754.81,0 10 +0.311369 0.078955 16 16.0 {669.12,0.4... 17248.12,0 10 +0.256310 0.201250 32 32.0 {10786.7,0.... 14987.98,0 10 finished run number of examples = 57 weighted example sum = 57.000000 weighted label sum = 57.000000 -average loss = 0.186361 +average loss = 0.231863 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_room_temp.stderr b/test/train-sets/ref/cats_room_temp.stderr index 0daabb527fd..588863b1aea 100644 --- a/test/train-sets/ref/cats_room_temp.stderr +++ b/test/train-sets/ref/cats_room_temp.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 -1095.824 1095.824 1 1.0 {0,25,0} 2.14,0.09 3 -547.9122 0.000000 2 2.0 {4.07,21.1,... 41.54,0 3 -273.9561 0.000000 4 4.0 {72.94,5.26,0} 88.24,0 3 -136.9780 0.000000 8 8.0 {67.13,2.93... 71.98,0 3 -68.48903 0.000000 16 16.0 {6.01,19.35,0} 51.44,0.09 3 -34.30662 0.124206 32 32.0 {59.57,0.92... 49.41,0.09 3 -18.39109 2.475568 64 64.0 {23.4,7.08,0} 50.56,0.09 3 +1095.824 1095.824 1 1.0 {0,25,0} 0,0 3 +582.1616 68.49878 2 2.0 {4.07,21.1,... 2.14,0.09 3 +291.0808 0.000000 4 4.0 {72.94,5.26,0} 41.54,0 3 +145.5404 0.000000 8 8.0 {67.13,2.93... 88.24,0 3 +72.80454 0.068661 16 16.0 {6.01,19.35,0} 71.98,0 3 +36.48046 0.156381 32 32.0 {59.57,0.92... 51.44,0.09 3 +18.24775 0.015050 64 64.0 {23.4,7.08,0} 49.41,0.09 3 finished run number of examples = 100 weighted example sum = 100.000000 weighted label sum = 100.000000 -average loss = 11.778620 +average loss = 11.691978 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; diff --git a/test/train-sets/ref/cats_save.stderr b/test/train-sets/ref/cats_save.stderr index b7204693915..b6ba4c67f41 100644 --- a/test/train-sets/ref/cats_save.stderr +++ b/test/train-sets/ref/cats_save.stderr @@ -17,10 +17,10 @@ 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} 0.61,0.41 6 -0.000000 0.000000 2 2.0 {0.58,0,0.41} 2.23,0.41 6 -0.000000 0.000000 4 4.0 {10.35,0,0.01} 22.59,0.01 6 -0.000000 0.000000 8 8.0 {2.41,0,0.41} 9.59,0.01 6 +0.000000 0.000000 1 1.0 {0,0,0.01} 0,0.01 6 +0.000000 0.000000 2 2.0 {0.58,0,0.41} 0.61,0.41 6 +0.000000 0.000000 4 4.0 {10.35,0,0.01} 2.23,0.41 6 +0.000000 0.000000 8 8.0 {2.41,0,0.41} 22.59,0.01 6 finished run number of examples = 10