diff --git a/KataGo/gtp_example.cfg b/KataGo/gtp_example.cfg
deleted file mode 100644
index b43ab80..0000000
--- a/KataGo/gtp_example.cfg
+++ /dev/null
@@ -1,305 +0,0 @@
-# Example config for C++ (non-python) gtp bot
-
-# RUNNING ON AN ONLINE SERVER OR IN A REAL TOURNAMENT OR MATCH:
-# If you plan to do so, you may want to read through the "Rules" section
-# below carefully for proper handling of komi and handicap games and end-of-game cleanup
-# and various other details.
-
-# NOTES ABOUT PERFORMANCE AND MEMORY USAGE:
-# You will likely want to tune one or more the following:
-#
-# numSearchThreads:
-# The number of CPU threads to use. If your GPU is powerful, it can actually be much higher than
-# the number of cores on your processor because you will need many threads to feed large enough
-# batches to make good use of the GPU.
-#
-# The "./katago benchmark" command can help you tune this parameter, as well as to test out the effect
-# of changes to any of the other parameters below!
-#
-# nnMaxBatchSize:
-# The maximum GPU batch size. Should often be at least as large as numSearchThreads.
-# Larger won't do anything, but also won't hurt except use a little bit more GPU memory.
-# Smaller can be fine if you have more than one GPU, since the GPUs will be sharing the work
-# of servicing the CPU threads.
-#
-# cudaUseFP16 and cudaUseNHWC:
-# These have a good chance of improving peformance at larger threads/batch sizes if
-# you are using the CUDA implementation with an NVIDIA GPU with FP16 tensor cores.
-#
-# nnCacheSizePowerOfTwo:
-# This controls the NN Cache size, which is the primary RAM/memory use.
-# Increase this if you don't mind the memory use and want better performance for searches with
-# tens of thousands of visits or more. Decrease this if you want to limit memory usage.
-#
-# If you're someone who is happy to do a bit of math - each neural net entry takes very
-# approximately 1.5KB, except when using whole-board ownership/territory visualizations, each
-# entry will take very approximately 3KB. The number of entries is (2 ** nnCacheSizePowerOfTwo),
-# for example 2 ** 18 = 262144.
-#
-# OTHER NOTES:
-# If you have more than one GPU, take a look at "OpenCL GPU settings" or "CUDA GPU settings" below.
-#
-# If using OpenCL, you will want to verify that KataGo is picking up the correct device!
-# (e.g. some systems may have both an Intel CPU OpenCL and GPU OpenCL, if KataGo appears to pick
-# the wrong one, you correct this by specifying "openclGpuToUse" below).
-#
-# You may also want to adjust "maxVisits", "ponderingEnabled", "resignThreshold", and possibly
-# other parameters depending on your intended usage.
-
-
-# Logs------------------------------------------------------------------------------------
-
-# Where to output log?
-logFile = gtp.log
-# Logging options
-logAllGTPCommunication = true
-logSearchInfo = true
-logToStderr = false
-
-# KataGo will display some info to stderr on GTP startup
-# Uncomment this to suppress that and remain silent
-# startupPrintMessageToStderr = false
-
-# Chat some stuff to stderr, for use in things like malkovich chat to OGS.
-# ogsChatToStderr = true
-
-# Configure the maximum length of analysis printed out by lz-analyze and other places.
-# Controls the number of moves after the first move in a variation.
-# analysisPVLen = 9
-
-# Report winrates for chat and analysis as (BLACK|WHITE|SIDETOMOVE).
-# Default is SIDETOMOVE, which is what tools that use LZ probably also expect
-# reportAnalysisWinratesAs = SIDETOMOVE
-
-# Default rules------------------------------------------------------------------------------------
-# See https://lightvector.github.io/KataGo/rules.html for a description of the rules.
-# These rules are defaults and can be changed mid-run by several custom GTP commands.
-# See https://github.com/lightvector/KataGo/blob/master/docs/GTP_Extensions.md for those commands.
-
-# koRule = SIMPLE       # Simple ko rules (triple ko = no result)
-koRule = POSITIONAL     # Positional superko
-# koRule = SITUATIONAL  # Situational superko
-
-scoringRule = AREA         # Area scoring
-# scoringRule = TERRITORY  # Territory scoring (uses a sort of special computer-friendly territory ruleset)
-
-taxRule = NONE    # All surrounded empty points are scored
-# taxRule = SEKI  # Eyes in seki do NOT count as points
-# taxRule = ALL   # All groups are taxed up to 2 points for the two eyes needed to live
-
-multiStoneSuicideLegal = true  #Is multiple-stone suicide legal? (Single-stone suicide is always illegal).
-
-hasButton = false # Set to true when area scoring to award 0.5 points to the first pass.
-
-whiteHandicapBonus = 0      # In handicap games, give white no compensation for black's handicap stones (Tromp-taylor, NZ, JP)
-# whiteHandicapBonus = N-1  # In handicap games, give white N-1 points for black's handicap stones (AGA)
-# whiteHandicapBonus = N    # In handicap games, give white N points for black's handicap stones (Chinese)
-
-# Bot behavior---------------------------------------------------------------------------------------
-
-# Resignation -------------
-
-# Resignation occurs if for at least resignConsecTurns in a row,
-# the winLossUtility (which is on a [-1,1] scale) is below resignThreshold.
-allowResignation = true
-resignThreshold = -0.98
-resignConsecTurns = 3
-
-# Handicap -------------
-
-# Assume that if black makes many moves in a row right at the start of the game, then the game is a handicap game.
-# This is necessary on some servers and for some GUIs and also when initializing from many SGF files, which may
-# set up a handicap games using repeated GTP "play" commands for black rather than GTP "place_free_handicap" commands.
-# However, it may also lead to incorrect understanding of komi if whiteHandicapBonus is used and a server does NOT
-# have such a practice.
-# Defaults to true! Uncomment and set to false to disable this behavior.
-# assumeMultipleStartingBlackMovesAreHandicap = true
-
-# Makes katago dynamically adjust to play more aggressively in handicap games based on the handicap and the current state of the game.
-# Comment to disable this and make KataGo play the same always.
-dynamicPlayoutDoublingAdvantageCapPerOppLead = 0.04
-# Instead of setting dynamicPlayoutDoublingAdvantageCapPerOppLead, you can uncomment these and set this to a value from -2.0 to 2.0
-# to set KataGo's aggression to a FIXED level.
-# Negative makes KataGo behave as if it is much weaker than the opponent, preferring to play defensively
-# Positive makes KataGo behave as if it is much stronger than the opponent, prefering to play aggressively or even overplay slightly.
-# playoutDoublingAdvantage = 0.0
-
-# Controls which side dynamicPlayoutDoublingAdvantageCapPerOppLead or playoutDoublingAdvantage applies to.
-playoutDoublingAdvantagePla = WHITE
-
-# Passing and cleanup -------------
-
-# Make the bot never assume that its pass will end the game, even if passing would end and "win" under Tromp-Taylor rules.
-# Usually this is a good idea when using it for analysis or playing on servers where scoring may be implemented non-tromp-taylorly.
-# Defaults to true! Uncomment and set to false to disable this.
-# conservativePass = true
-
-# When playing under territory scoring, encourage the bot to fill dame before passing.
-# This is NOT absolutely guaranteed to work, and possibly in some rare pathological situations will make the bot
-# play a bad move, losing points. However, it also acts as a safeguard against things like a situation when the opponent must
-# eventually make a protective move and lose 1 point, where the bot might otherwise assume that the score would be counted as such,
-# yet without filling the dame to force the opponent to actually make the move.
-# Defaults to true! Uncomment and set to false to disable this.
-# fillDameBeforePass = true
-
-# When using territory scoring, self-play games continue beyond two passes with special cleanup
-# rules that may be confusing for human players. This option prevents the special cleanup phases from being
-# reachable when using the bot for GTP play.
-# Defaults to true! Uncomment and set to false if you want KataGo to be able to enter special cleanup.
-# For example, if you are testing it against itself, or against another bot that has precisely implemented the rules
-# documented at https://lightvector.github.io/KataGo/rules.html
-# preventCleanupPhase = true
-
-
-# Search limits-----------------------------------------------------------------------------------
-
-# If provided, limit maximum number of root visits per search to this much. (With tree reuse, visits do count earlier search)
-maxVisits = 500
-# If provided, limit maximum number of new playouts per search to this much. (With tree reuse, playouts do not count earlier search)
-# maxPlayouts = 300
-# If provided, cap search time at this many seconds (search will still try to follow GTP time controls)
-# maxTime = 60
-
-# Ponder on the opponent's turn?
-ponderingEnabled = false
-
-# Same limits but for ponder searches if pondering is enabled
-# maxVisitsPondering = 1000
-# maxPlayoutsPondering = 1000
-# maxTimePondering = 60
-
-# Number of seconds to buffer for lag for GTP time controls
-lagBuffer = 1.0
-
-# Number of threads to use in search
-numSearchThreads = 1
-
-# Play a little faster if the opponent is passing, for friendliness
-searchFactorAfterOnePass = 0.50
-searchFactorAfterTwoPass = 0.25
-# Play a little faster if super-winning, for friendliess
-searchFactorWhenWinning = 0.40
-searchFactorWhenWinningThreshold = 0.95
-
-# GPU Settings-------------------------------------------------------------------------------
-
-# Maximum number of positions to send to GPU at once. Note that you will also need to increase numSearchThreads
-# to make use of this, as every thread in KataGo is synchronous, so with 1 thread max batch will only be 1 anyways.
-nnMaxBatchSize = 16
-# Cache up to 2 ** this many neural net evaluations in case of transpositions in the tree.
-nnCacheSizePowerOfTwo = 19
-# Size of mutex pool for nnCache is 2 ** this
-nnMutexPoolSizePowerOfTwo = 15
-# Randomize board orientation when running neural net evals?
-nnRandomize = true
-# If provided, force usage of a specific seed for nnRandomize instead of randomizing
-# nnRandSeed = abcdefg
-
-# How many threads should there be to feed positions to the neural net?
-# Server threads are indexed 0,1,...(n-1) for the purposes of the below GPU settings arguments
-# that specify which threads should use which GPUs.
-# NOTE: This parameter is probably ONLY useful if you have multiple GPUs, since each GPU will need a thread.
-# If you're tuning single-GPU performance, use numSearchThreads instead.
-numNNServerThreadsPerModel = 1
-
-# CUDA GPU settings--------------------------------------
-# These only apply when using CUDA as the backend for inference.
-# (For GTP, we only ever have one model, when playing matches, we might have more than one, see match_example.cfg)
-
-# Default behavior tries to guess the 'best' GPU or device
-# You will want to uncomment and adjust one or more of these lines to take advantage of a multi-gpu machine
-# cudaDeviceToUse = 0 #use device 0 for all server threads (numNNServerThreadsPerModel) unless otherwise specified per-model or per-thread-per-model
-# cudaDeviceToUseModel0 = 3 #use device 3 for model 0 for all threads unless otherwise specified per-thread for this model
-# cudaDeviceToUseModel1 = 2 #use device 2 for model 1 for all threads unless otherwise specified per-thread for this model
-# cudaDeviceToUseModel0Thread0 = 3 #use device 3 for model 0, server thread 0
-# cudaDeviceToUseModel0Thread1 = 2 #use device 2 for model 0, server thread 1
-
-# Uncomment these on NVIDIA devices with FP16 tensor cores for probably a speedup, at the cost of introducing some precision loss in the nn calculation.
-# cudaUseFP16 = true
-# cudaUseNHWC = true
-
-# OpenCL GPU settings--------------------------------------
-# These only apply when using OpenCL as the backend for inference.
-# (For GTP, we only ever have one model, when playing matches, we might have more than one, see match_example.cfg)
-
-# Default behavior tries to guess the 'best' GPU or device
-# You will want to uncomment and adjust one or more of these lines to take advantage of a multi-gpu machine
-# openclDeviceToUse = 0 #use device 0 for all server threads (numNNServerThreadsPerModel) unless otherwise specified per-model or per-thread-per-model
-# openclDeviceToUseModel0 = 3 #use device 3 for model 0 for all threads unless otherwise specified per-thread for this model
-# openclDeviceToUseModel1 = 2 #use device 2 for model 1 for all threads unless otherwise specified per-thread for this model
-# openclDeviceToUseModel0Thread0 = 3 #use device 3 for model 0, server thread 0
-# openclDeviceToUseModel0Thread1 = 2 #use device 2 for model 0, server thread 1
-
-# Uncomment to tune OpenCL for every board size separately, rather than only the largest possible size
-# openclReTunePerBoardSize = true
-
-# Root move selection and biases------------------------------------------------------------------------------
-
-# If provided, force usage of a specific seed for various things in the search instead of randomizing
-# searchRandSeed = hijklmn
-
-# Temperature for the early game, randomize between chosen moves with this temperature
-chosenMoveTemperatureEarly = 0.5
-# Decay temperature for the early game by 0.5 every this many moves, scaled with board size.
-chosenMoveTemperatureHalflife = 19
-# At the end of search after the early game, randomize between chosen moves with this temperature
-chosenMoveTemperature = 0.10
-# Subtract this many visits from each move prior to applying chosenMoveTemperature
-# (unless all moves have too few visits) to downweight unlikely moves
-chosenMoveSubtract = 0
-# The same as chosenMoveSubtract but only prunes moves that fall below the threshold, does not affect moves above
-chosenMovePrune = 1
-
-# Use dirichlet noise for the root node policy?
-rootNoiseEnabled = false
-# Dirichlet noise alpha is set to this divided by number of legal moves. 10.83 produces an alpha of 0.03 on an empty 19x19 board.
-rootDirichletNoiseTotalConcentration = 10.83
-# Proportion of root policy that is noise
-rootDirichletNoiseWeight = 0.25
-
-# Number of symmetries to sample (WITH replacement) and average at the root
-rootNumSymmetriesToSample = 1
-
-# Using LCB for move selection?
-useLcbForSelection = true
-# How many stdevs a move needs to be better than another for LCB selection
-lcbStdevs = 5.0
-# Only use LCB override when a move has this proportion of visits as the top move
-minVisitPropForLCB = 0.15
-
-# Internal params------------------------------------------------------------------------------
-
-# Scales the utility of winning/losing
-winLossUtilityFactor = 1.0
-# Scales the utility for trying to maximize score
-staticScoreUtilityFactor = 0.10
-dynamicScoreUtilityFactor = 0.30
-# Adjust dynamic score center this proportion of the way towards zero, capped at a reasonable amount.
-dynamicScoreCenterZeroWeight = 0.20
-dynamicScoreCenterScale = 0.75
-# The utility of getting a "no result" due to triple ko or other long cycle in non-superko rulesets (-1 to 1)
-noResultUtilityForWhite = 0.0
-# The number of wins that a draw counts as, for white. (0 to 1)
-drawEquivalentWinsForWhite = 0.5
-
-# Exploration constant for mcts
-cpuctExploration = 0.9
-cpuctExplorationLog = 0.6
-# FPU reduction constant for mcts
-fpuReductionMax = 0.2
-rootFpuReductionMax = 0.1
-# Use parent average value for fpu base point instead of point value net estimate
-fpuUseParentAverage = true
-# Amount to apply a downweighting of children with very bad values relative to good ones
-valueWeightExponent = 0.5
-# Slight incentive for the bot to behave human-like with regard to passing at the end, filling the dame,
-# not wasting time playing in its own territory, etc, and not play moves that are equivalent in terms of
-# points but a bit more unfriendly to humans.
-rootEndingBonusPoints = 0.5
-# Make the bot prune useless moves that are just prolonging the game to avoid losing yet
-rootPruneUselessMoves = true
-
-# How big to make the mutex pool for search synchronization
-mutexPoolSize = 8192
-# How many virtual losses to add when a thread descends through a node
-numVirtualLossesPerThread = 1
diff --git a/KataGo/japanese_explore_score.cfg b/KataGo/japanese_explore_score.cfg
deleted file mode 100644
index c6f5924..0000000
--- a/KataGo/japanese_explore_score.cfg
+++ /dev/null
@@ -1,305 +0,0 @@
-# Example config for C++ (non-python) gtp bot
-
-# RUNNING ON AN ONLINE SERVER OR IN A REAL TOURNAMENT OR MATCH:
-# If you plan to do so, you may want to read through the "Rules" section
-# below carefully for proper handling of komi and handicap games and end-of-game cleanup
-# and various other details.
-
-# NOTES ABOUT PERFORMANCE AND MEMORY USAGE:
-# You will likely want to tune one or more the following:
-#
-# numSearchThreads:
-# The number of CPU threads to use. If your GPU is powerful, it can actually be much higher than
-# the number of cores on your processor because you will need many threads to feed large enough
-# batches to make good use of the GPU.
-#
-# The "./katago benchmark" command can help you tune this parameter, as well as to test out the effect
-# of changes to any of the other parameters below!
-#
-# nnMaxBatchSize:
-# The maximum GPU batch size. Should often be at least as large as numSearchThreads.
-# Larger won't do anything, but also won't hurt except use a little bit more GPU memory.
-# Smaller can be fine if you have more than one GPU, since the GPUs will be sharing the work
-# of servicing the CPU threads.
-#
-# cudaUseFP16 and cudaUseNHWC:
-# These have a good chance of improving peformance at larger threads/batch sizes if
-# you are using the CUDA implementation with an NVIDIA GPU with FP16 tensor cores.
-#
-# nnCacheSizePowerOfTwo:
-# This controls the NN Cache size, which is the primary RAM/memory use.
-# Increase this if you don't mind the memory use and want better performance for searches with
-# tens of thousands of visits or more. Decrease this if you want to limit memory usage.
-#
-# If you're someone who is happy to do a bit of math - each neural net entry takes very
-# approximately 1.5KB, except when using whole-board ownership/territory visualizations, each
-# entry will take very approximately 3KB. The number of entries is (2 ** nnCacheSizePowerOfTwo),
-# for example 2 ** 18 = 262144.
-#
-# OTHER NOTES:
-# If you have more than one GPU, take a look at "OpenCL GPU settings" or "CUDA GPU settings" below.
-#
-# If using OpenCL, you will want to verify that KataGo is picking up the correct device!
-# (e.g. some systems may have both an Intel CPU OpenCL and GPU OpenCL, if KataGo appears to pick
-# the wrong one, you correct this by specifying "openclGpuToUse" below).
-#
-# You may also want to adjust "maxVisits", "ponderingEnabled", "resignThreshold", and possibly
-# other parameters depending on your intended usage.
-
-
-# Logs------------------------------------------------------------------------------------
-
-# Where to output log?
-logFile = gtp.log
-# Logging options
-logAllGTPCommunication = true
-logSearchInfo = true
-logToStderr = false
-
-# KataGo will display some info to stderr on GTP startup
-# Uncomment this to suppress that and remain silent
-# startupPrintMessageToStderr = false
-
-# Chat some stuff to stderr, for use in things like malkovich chat to OGS.
-# ogsChatToStderr = true
-
-# Configure the maximum length of analysis printed out by lz-analyze and other places.
-# Controls the number of moves after the first move in a variation.
-# analysisPVLen = 9
-
-# Report winrates for chat and analysis as (BLACK|WHITE|SIDETOMOVE).
-# Default is SIDETOMOVE, which is what tools that use LZ probably also expect
-# reportAnalysisWinratesAs = SIDETOMOVE
-
-# Default rules------------------------------------------------------------------------------------
-# See https://lightvector.github.io/KataGo/rules.html for a description of the rules.
-# These rules are defaults and can be changed mid-run by several custom GTP commands.
-# See https://github.com/lightvector/KataGo/blob/master/docs/GTP_Extensions.md for those commands.
-
-koRule = SIMPLE       # Simple ko rules (triple ko = no result)
-# koRule = POSITIONAL     # Positional superko
-# koRule = SITUATIONAL  # Situational superko
-
-# scoringRule = AREA         # Area scoring
-scoringRule = TERRITORY  # Territory scoring (uses a sort of special computer-friendly territory ruleset)
-
-# taxRule = NONE    # All surrounded empty points are scored
-taxRule = SEKI  # Eyes in seki do NOT count as points
-# taxRule = ALL   # All groups are taxed up to 2 points for the two eyes needed to live
-
-multiStoneSuicideLegal = false  #Is multiple-stone suicide legal? (Single-stone suicide is always illegal).
-
-hasButton = false # Set to true when area scoring to award 0.5 points to the first pass.
-
-whiteHandicapBonus = 0      # In handicap games, give white no compensation for black's handicap stones (Tromp-taylor, NZ, JP)
-# whiteHandicapBonus = N-1  # In handicap games, give white N-1 points for black's handicap stones (AGA)
-# whiteHandicapBonus = N    # In handicap games, give white N points for black's handicap stones (Chinese)
-
-# Bot behavior---------------------------------------------------------------------------------------
-
-# Resignation -------------
-
-# Resignation occurs if for at least resignConsecTurns in a row,
-# the winLossUtility (which is on a [-1,1] scale) is below resignThreshold.
-allowResignation = true
-resignThreshold = -0.999
-resignConsecTurns = 3
-
-# Handicap -------------
-
-# Assume that if black makes many moves in a row right at the start of the game, then the game is a handicap game.
-# This is necessary on some servers and for some GUIs and also when initializing from many SGF files, which may
-# set up a handicap games using repeated GTP "play" commands for black rather than GTP "place_free_handicap" commands.
-# However, it may also lead to incorrect understanding of komi if whiteHandicapBonus is used and a server does NOT
-# have such a practice.
-# Defaults to true! Uncomment and set to false to disable this behavior.
-# assumeMultipleStartingBlackMovesAreHandicap = true
-
-# Makes katago dynamically adjust to play more aggressively in handicap games based on the handicap and the current state of the game.
-# Comment to disable this and make KataGo play the same always.
-dynamicPlayoutDoublingAdvantageCapPerOppLead = 0.04
-# Instead of setting dynamicPlayoutDoublingAdvantageCapPerOppLead, you can uncomment these and set this to a value from -2.0 to 2.0
-# to set KataGo's aggression to a FIXED level.
-# Negative makes KataGo behave as if it is much weaker than the opponent, preferring to play defensively
-# Positive makes KataGo behave as if it is much stronger than the opponent, prefering to play aggressively or even overplay slightly.
-# playoutDoublingAdvantage = 0.0
-
-# Controls which side dynamicPlayoutDoublingAdvantageCapPerOppLead or playoutDoublingAdvantage applies to.
-playoutDoublingAdvantagePla = WHITE
-
-# Passing and cleanup -------------
-
-# Make the bot never assume that its pass will end the game, even if passing would end and "win" under Tromp-Taylor rules.
-# Usually this is a good idea when using it for analysis or playing on servers where scoring may be implemented non-tromp-taylorly.
-# Defaults to true! Uncomment and set to false to disable this.
-conservativePass = true
-
-# When playing under territory scoring, encourage the bot to fill dame before passing.
-# This is NOT absolutely guaranteed to work, and possibly in some rare pathological situations will make the bot
-# play a bad move, losing points. However, it also acts as a safeguard against things like a situation when the opponent must
-# eventually make a protective move and lose 1 point, where the bot might otherwise assume that the score would be counted as such,
-# yet without filling the dame to force the opponent to actually make the move.
-# Defaults to true! Uncomment and set to false to disable this.
-# fillDameBeforePass = true
-
-# When using territory scoring, self-play games continue beyond two passes with special cleanup
-# rules that may be confusing for human players. This option prevents the special cleanup phases from being
-# reachable when using the bot for GTP play.
-# Defaults to true! Uncomment and set to false if you want KataGo to be able to enter special cleanup.
-# For example, if you are testing it against itself, or against another bot that has precisely implemented the rules
-# documented at https://lightvector.github.io/KataGo/rules.html
-# preventCleanupPhase = true
-
-
-# Search limits-----------------------------------------------------------------------------------
-
-# If provided, limit maximum number of root visits per search to this much. (With tree reuse, visits do count earlier search)
-maxVisits = 2500
-# If provided, limit maximum number of new playouts per search to this much. (With tree reuse, playouts do not count earlier search)
-# maxPlayouts = 300
-# If provided, cap search time at this many seconds (search will still try to follow GTP time controls)
-# maxTime = 60
-
-# Ponder on the opponent's turn?
-ponderingEnabled = false
-
-# Same limits but for ponder searches if pondering is enabled
-# maxVisitsPondering = 1000
-# maxPlayoutsPondering = 1000
-# maxTimePondering = 60
-
-# Number of seconds to buffer for lag for GTP time controls
-lagBuffer = 1.0
-
-# Number of threads to use in search
-numSearchThreads = 4
-
-# Play a little faster if the opponent is passing, for friendliness
-searchFactorAfterOnePass = 0.50
-searchFactorAfterTwoPass = 0.25
-# Play a little faster if super-winning, for friendliess
-searchFactorWhenWinning = 0.40
-searchFactorWhenWinningThreshold = 0.95
-
-# GPU Settings-------------------------------------------------------------------------------
-
-# Maximum number of positions to send to GPU at once. Note that you will also need to increase numSearchThreads
-# to make use of this, as every thread in KataGo is synchronous, so with 1 thread max batch will only be 1 anyways.
-nnMaxBatchSize = 16
-# Cache up to 2 ** this many neural net evaluations in case of transpositions in the tree.
-nnCacheSizePowerOfTwo = 20
-# Size of mutex pool for nnCache is 2 ** this
-nnMutexPoolSizePowerOfTwo = 16
-# Randomize board orientation when running neural net evals?
-nnRandomize = true
-# If provided, force usage of a specific seed for nnRandomize instead of randomizing
-# nnRandSeed = abcdefg
-
-# How many threads should there be to feed positions to the neural net?
-# Server threads are indexed 0,1,...(n-1) for the purposes of the below GPU settings arguments
-# that specify which threads should use which GPUs.
-# NOTE: This parameter is probably ONLY useful if you have multiple GPUs, since each GPU will need a thread.
-# If you're tuning single-GPU performance, use numSearchThreads instead.
-numNNServerThreadsPerModel = 1
-
-# CUDA GPU settings--------------------------------------
-# These only apply when using CUDA as the backend for inference.
-# (For GTP, we only ever have one model, when playing matches, we might have more than one, see match_example.cfg)
-
-# Default behavior tries to guess the 'best' GPU or device
-# You will want to uncomment and adjust one or more of these lines to take advantage of a multi-gpu machine
-# cudaDeviceToUse = 0 #use device 0 for all server threads (numNNServerThreadsPerModel) unless otherwise specified per-model or per-thread-per-model
-# cudaDeviceToUseModel0 = 3 #use device 3 for model 0 for all threads unless otherwise specified per-thread for this model
-# cudaDeviceToUseModel1 = 2 #use device 2 for model 1 for all threads unless otherwise specified per-thread for this model
-# cudaDeviceToUseModel0Thread0 = 3 #use device 3 for model 0, server thread 0
-# cudaDeviceToUseModel0Thread1 = 2 #use device 2 for model 0, server thread 1
-
-# Uncomment these on NVIDIA devices with FP16 tensor cores for probably a speedup, at the cost of introducing some precision loss in the nn calculation.
-# cudaUseFP16 = true
-# cudaUseNHWC = true
-
-# OpenCL GPU settings--------------------------------------
-# These only apply when using OpenCL as the backend for inference.
-# (For GTP, we only ever have one model, when playing matches, we might have more than one, see match_example.cfg)
-
-# Default behavior tries to guess the 'best' GPU or device
-# You will want to uncomment and adjust one or more of these lines to take advantage of a multi-gpu machine
-# openclDeviceToUse = 0 #use device 0 for all server threads (numNNServerThreadsPerModel) unless otherwise specified per-model or per-thread-per-model
-# openclDeviceToUseModel0 = 3 #use device 3 for model 0 for all threads unless otherwise specified per-thread for this model
-# openclDeviceToUseModel1 = 2 #use device 2 for model 1 for all threads unless otherwise specified per-thread for this model
-# openclDeviceToUseModel0Thread0 = 3 #use device 3 for model 0, server thread 0
-# openclDeviceToUseModel0Thread1 = 2 #use device 2 for model 0, server thread 1
-
-# Uncomment to tune OpenCL for every board size separately, rather than only the largest possible size
-# openclReTunePerBoardSize = true
-
-# Root move selection and biases------------------------------------------------------------------------------
-
-# If provided, force usage of a specific seed for various things in the search instead of randomizing
-# searchRandSeed = hijklmn
-
-# Temperature for the early game, randomize between chosen moves with this temperature
-chosenMoveTemperatureEarly = 0.5
-# Decay temperature for the early game by 0.5 every this many moves, scaled with board size.
-chosenMoveTemperatureHalflife = 19
-# At the end of search after the early game, randomize between chosen moves with this temperature
-chosenMoveTemperature = 0.10
-# Subtract this many visits from each move prior to applying chosenMoveTemperature
-# (unless all moves have too few visits) to downweight unlikely moves
-chosenMoveSubtract = 0
-# The same as chosenMoveSubtract but only prunes moves that fall below the threshold, does not affect moves above
-chosenMovePrune = 1
-
-# Use dirichlet noise for the root node policy?
-rootNoiseEnabled = false
-# Dirichlet noise alpha is set to this divided by number of legal moves. 10.83 produces an alpha of 0.03 on an empty 19x19 board.
-rootDirichletNoiseTotalConcentration = 10.83
-# Proportion of root policy that is noise
-rootDirichletNoiseWeight = 0.25
-
-# Number of symmetries to sample (WITH replacement) and average at the root
-rootNumSymmetriesToSample = 1
-
-# Using LCB for move selection?
-useLcbForSelection = true
-# How many stdevs a move needs to be better than another for LCB selection
-lcbStdevs = 5.0
-# Only use LCB override when a move has this proportion of visits as the top move
-minVisitPropForLCB = 0.15
-
-# Internal params------------------------------------------------------------------------------
-
-# Scales the utility of winning/losing
-winLossUtilityFactor = 0.4
-# Scales the utility for trying to maximize score
-staticScoreUtilityFactor = 0.2
-dynamicScoreUtilityFactor = 0.4
-# Adjust dynamic score center this proportion of the way towards zero, capped at a reasonable amount.
-dynamicScoreCenterZeroWeight = 0.20
-dynamicScoreCenterScale = 0.75
-# The utility of getting a "no result" due to triple ko or other long cycle in non-superko rulesets (-1 to 1)
-noResultUtilityForWhite = 0.0
-# The number of wins that a draw counts as, for white. (0 to 1)
-drawEquivalentWinsForWhite = 0.5
-
-# Exploration constant for mcts
-cpuctExploration = 2.0
-cpuctExplorationLog = 0.8
-# FPU reduction constant for mcts
-fpuReductionMax = 0.2
-rootFpuReductionMax = 0.1
-# Use parent average value for fpu base point instead of point value net estimate
-fpuUseParentAverage = true
-# Amount to apply a downweighting of children with very bad values relative to good ones
-valueWeightExponent = 0.5
-# Slight incentive for the bot to behave human-like with regard to passing at the end, filling the dame,
-# not wasting time playing in its own territory, etc, and not play moves that are equivalent in terms of
-# points but a bit more unfriendly to humans.
-rootEndingBonusPoints = 0.5
-# Make the bot prune useless moves that are just prolonging the game to avoid losing yet
-rootPruneUselessMoves = true
-
-# How big to make the mutex pool for search synchronization
-mutexPoolSize = 8192
-# How many virtual losses to add when a thread descends through a node
-numVirtualLossesPerThread = 1
diff --git a/KataGo/katago b/KataGo/katago
deleted file mode 100755
index dd0783b..0000000
Binary files a/KataGo/katago and /dev/null differ
diff --git a/README.md b/README.md
index 7a2d9b5..2295828 100644
--- a/README.md
+++ b/README.md
@@ -4,36 +4,38 @@ Manual
 Installation
 ------------
 * pip install kivy
-* change the `engine.command` field in `config.json` to your kata installation (for example, to `path/to/lizzie/katago/katago.exe`)
-* start the app by running `python katrain.py` (or `python3` if needed)
+* On linux, change the `engine.command` field in `config.json` to your kata v1.3+ installation.
+* start the app by running `python katrain.py`
 
 Options
 -------
-* Top row options
+* Check box options
     * Eval: show the coloured dots on the moves for this player.
     * Hints: show suggested moves for this player.
     * Undo: automatically undo poor moves for this player and make them try again.
+    * AI: let the AI control this player. Check both for self-play.
+    * Show owner: show expected control of territory.    
     * Lock AI: disallow extra undos, changing hints options, changing auto move, or AI move.
-    * Show owner: show expected control of territory. 
+    * Fast: use a lower number of max visits for evaluation/AI move.
+    * Balance score: Deliberately make sub-optimal moves as the AI in an attempt to balance the score towawrds a slight win.
 
 * Temperature/Evaluation/Score: Not that these fields can be hidden by clicking on the text.
     * Temperature is the point difference between passing and the best move.
     * Evaluation is where on this scale the last move was, from 0% (equivalent to a pass) to 100% (best move). 
     This can be < 0% in case of suicidal moves, or >100% when Kata did not consider the move before, or further analysis shows it to be better than the best one considered.
-    * Score: Expected score.
+    * Score: How far one player is ahead.
 
 Play
 ----
 
 * Play against the AI
-    * Turn on auto move. 
+    * Turn on AI for the chosen player. 
     * Choose whether to turn on `balance score` to make the AI play slack moves.
     * Choose whether to turn on `undo` for your colour to be prompted to re-try poor moves. 
     * Choose whether or not to turn on `fast` to make the AI play faster but read less deeply (NB: with balance score, faster AI can be a stronger opponent, as there are fewer mediocre moves considered).
     * Possibly lock AI to prevent yourself from peeking at hints, etc.
     * Possibly hide score or temperature.
-    * Possibly hide evaluation for the AI player.
-    * Play by playing a move or clicking AI move if you want white.
+    * If you chose AI to play black, click AI move for the first move.
     
 * Engine-assisted play
     * Turn off auto move.
@@ -43,7 +45,7 @@ Play
     * Play with a friend with instant feedback and/or undos for both, or see how many stones stronger you are with one undo. (But please play unranked and be honest to your opponent on what you're doing) 
 
 * Analysis
-    * Copy the SGF into the text box
+    * Copy the SGF into the text box. Note that branches are not supported and will lead to strange results.
     * Choose whether or not to turn on `fast` to make the AI weaker but analyze faster.
     * Click `Analyze`
     
@@ -61,9 +63,7 @@ The `trainer` block has the following options to tweak:
 * `balance_play_min_eval`: when needing to balance score, the AI will pick a move which is at least this good.
 * `balance_play_min_visits`: never pick a move with fewer playouts than this.
 * `undo_eval_threshold`, `undo_point_threshold`: prompt player to undo if move is worse than this in terms of points AND evaluation.
-* `undo_outdated_eval_threshold`: don't prompt undo if last move's evaluation is >= `undo_eval_threshold` and the NEW evaluation is greater than this. (this decreases frustration when hints are on, or when kata over-estimates the best move).
 * `num_undo_prompts`: automatically undo bad moves when `undo` is on at most this many times.
-* `show_ai_options`: show which moves the AI considered.
 
 The cfg file has additional configuration for kata. In particular, it changes the default to being more exploratory and score-based (and therefore nicer as an opponent, but weaker as analysis tool).
 
diff --git a/board.py b/board.py
index 8dd23ae..ea9a6c3 100644
--- a/board.py
+++ b/board.py
@@ -17,6 +17,8 @@ class Move:
         self.analysis = None
         self.pass_analysis = None
         self.ownership = None
+        self.x_comment = ""
+        self.auto_undid = False
         self.move_number = 0
 
     def __repr__(self):
@@ -61,11 +63,13 @@ class Move:
         score = score or self.score
         return f"{'B' if score >= 0 else 'W'}+{abs(score):.1f}"
 
-    @property
-    def comment(self,sgf=False):
+    def comment(self,sgf=False, eval=False, hints=False):
         if not self.parent: # root
             return ""
-        text = f"Move {self.move_number}: {self.bw_player()} @ {self.gtp()}  {'(AI Move)' if self.robot else ''}\n"
+        text = f"Move {self.move_number}: {self.bw_player()} {self.gtp()}  {'(AI Move)' if self.robot else ''}\n"
+        text += self.x_comment
+        text += "".join(f"Auto undid move {m.gtp()} ({m.evaluation*100:.1f}% efficient)\n" for m in self.children if m.auto_undid)
+
         if self.analysis_ready:
             score, _, temperature = self.temperature_stats
             if sgf:
@@ -73,13 +77,14 @@ class Move:
                 text += f"Temperature: {temperature:.1f}\n"
             if self.parent and self.parent.analysis_ready:
                 prev_best_score, prev_worst_score, prev_temperature = self.parent.temperature_stats
-                text += f"Top move was {self.format_score(prev_best_score)} @ {self.parent.analysis[0]['move']}\n"
-                text += f"Pass score was {self.format_score(prev_worst_score)}\n"
+                if sgf or hints:
+                    text += f"Top move was {self.parent.analysis[0]['move']} ({self.format_score(prev_best_score)})\n"
+                    text += f"Pass score was {self.format_score(prev_worst_score)}\n"
                 if prev_temperature < 0.5:
                     text += f"Previous temperature ({prev_temperature}) too low for evaluation\n"
                 else:
-                    if eval:
-                        text += f"Evaluation: {100*self.evaluation:.1f}%\n"
+                    if sgf or eval:
+                        text += f"Evaluation: {100*self.evaluation:.1f}% efficient\n"
                         outdated_evaluation = self.outdated_evaluation
                         if outdated_evaluation and outdated_evaluation > self.evaluation and outdated_evaluation > self.evaluation + 0.01:
                             text += f"(Was considered last move as: {100 * outdated_evaluation :.1f}%)\n"
@@ -94,7 +99,7 @@ class Move:
     @property
     def evaluation_info(self):
         if self.parent and self.parent.analysis_ready and self.analysis_ready:
-            return self.evaluation,self.parent.temperature_stats[2]
+            return self.evaluation, self.parent.temperature_stats[2]
         else:
             return None,None
 
@@ -264,7 +269,7 @@ class Board:
     def moves(self) -> list:  # flat list of moves to current
         moves = []
         p = self.current_move
-        while p != self.root:
+        while p is not self.root:  # NB == is wrong here
             moves.append(p)
             p = p.parent
         return moves[::-1]
diff --git a/config.json b/config.json
index e283a60..067b76c 100644
--- a/config.json
+++ b/config.json
@@ -3,12 +3,11 @@
     "pass_visits": 200,
     "pass_visits_fast": 50,
     "visits": 3500,
-    "visits_fast": 1500,
-    "nopass_visits": 10
+    "visits_fast": 1500
   },
   "board": {
     "size": 19,
-    "komi": 7.5
+    "komi": 6.5
   },
   "ui": {
     "size_min": 1,
@@ -35,7 +34,6 @@
     "balance_play_min_eval": 0.875,
     "balance_play_min_visits": 20,
     "undo_eval_threshold": 0.875,
-    "undo_outdated_eval_threshold": 0.8,
     "undo_point_threshold": 1,
     "num_undo_prompts": 1
   },
diff --git a/controller.py b/controller.py
index f493fb0..7beb723 100644
--- a/controller.py
+++ b/controller.py
@@ -26,7 +26,6 @@ class EngineControls(GridLayout):
             [analysis_settings["pass_visits"], analysis_settings["visits"]],
             [analysis_settings["pass_visits_fast"], analysis_settings["visits_fast"]],
         ]
-        self.min_nopass_visits = analysis_settings["nopass_visits"]
         self.train_settings = Config.get("trainer")
         self.debug = Config.get("debug")["level"]
         self.board_size = Config.get("board")["size"]
@@ -78,13 +77,11 @@ class EngineControls(GridLayout):
             print(str(e))
             self.info.text = f"Illegal move: {str(e)}"
             return
-        print("PLAYED", move, self.board.stones)
         self._request_analysis(mr)
         return mr
 
     # engine action functions
     def _do_play(self, *args):
-        print("CURRENT PLAYER", self.board.current_player)
         move = Move(player=self.board.current_player, coords=args[0])
         self.play(move)
         # mr.waiting_for_analysis
@@ -93,66 +90,34 @@ class EngineControls(GridLayout):
     def update_evaluation(self):
         current_move = self.board.current_move
         if self.eval.active(current_move.player):
-            self.info.text = current_move.comment
+            self.info.text = current_move.comment(eval=self.eval.active(current_move.player), hints=self.hints.active(current_move.player))
         self.evaluation.text = ''
-        if current_move.analysis_ready:
+        if current_move.analysis_ready and self.eval.active(current_move.player):
             self.score.text = current_move.format_score().replace("-", "\u2013")
             self.temperature.text = f"{current_move.temperature_stats[2]:.1f}"
             if current_move.parent and current_move.parent.analysis_ready:
                 self.evaluation.text = f"{100 * current_move.evaluation:.1f}%"
 
-        # when to trigger auto undo?
-#      self.undo.disabled = True  # undo while waiting for this does weird things
-#      undid = False
-#       self.info.text = ""
-#        if self.auto_undo.active(1 - self.board.current_player):
-#            undid = self._auto_undo(move)
-#        if self.ai_auto.active and not undid:
-#            self._do_aimove(move, True)
-#        self.undo.disabled = False
-
-    def _auto_undo(self, move):
-        ts = self.train_settings
-        self.info.text = "Evaluating..."
-        if (
-            move.evaluation
-            and move.evaluation < ts["undo_eval_threshold"]
-            and move.points_lost >= ts["undo_point_threshold"]
-            and ts["num_undo_prompts"] > 0
-        ):
-            if move.outdated_evaluation:
-                outdated_points_lost = (1 - move.outdated_evaluation) * move.points_lost / (1 - move.evaluation)
-            # so if the move was not that far off (>undo_outdated_eval_threshold) and according to last move's analysis it was fine, don't undo.
-            if (
-                move.outdated_evaluation
-                and (
-                    move.outdated_evaluation >= ts["undo_eval_threshold"]
-                    or outdated_points_lost < ts["undo_point_threshold"]
-                )
-                and (
-                    move.evaluation > ts["undo_outdated_eval_threshold"]
-                    or outdated_points_lost < ts["undo_point_threshold"]
-                )
-            ):
-                self.info.text += f"\nBut according to my previous evaluation it was {move.outdated_evaluation*100:.1f}% effective and lost {outdated_points_lost:.1f} point(s), so let's continue anyway.\n"
-            else:
-                if len(self.board.current_move.parent.children) <= ts["num_undo_prompts"]:
-                    self.info.text += f"\nLet's try again.\n"
+        if current_move.analysis_ready and current_move.parent and current_move.parent.analysis_ready and not current_move.children:
+            # handle automatic undo
+            if self.auto_undo.active(current_move.player) and not self.ai_auto.active(current_move.player) and not current_move.auto_undid:
+                ts = self.train_settings
+                # TODO: is this overly generous wrt low visit outdated evaluations?
+                eval = max(current_move.evaluation, current_move.outdated_evaluation or 0)
+                points_lost = (current_move.parent or current_move).temperature_stats[2] * (1 - eval)
+                if eval < ts["undo_eval_threshold"] and points_lost >= ts["undo_point_threshold"]:
+                    current_move.auto_undid = True
                     self.board.undo()
-                    return True
-                else:
-                    evaled_moves = sorted(
-                        [m for m in self.board.current_move.parent.children if m.evaluation],
-                        key=lambda m: -m.evaluation,
-                    )
-                    if evaled_moves and evaled_moves[0].coords != move.coords:
-                        self.board.undo()
-                        self.board.play(evaled_moves[0])
-                    summary = "\n".join(f"{m.gtp()}: {100*m.evaluation:.1f}% effective" for m in evaled_moves)
-                    self.info.text += f"\nYour moves:\n{summary}.\nLet's continue with {evaled_moves[0].gtp()}.\n"
-        return False
+                    if len(current_move.parent.children) >= ts["num_undo_prompts"] + 1:
+                        best_move = sorted([m for m in current_move.parent.children], key=lambda m: -(m.evaluation_info[0] or 0) )[0]
+                        best_move.x_comment = f"Automatically played as best option after max. {ts['num_undo_prompts']} undo(s).\n"
+                        self.board.play(best_move)
+                    self.update_evaluation()
+            # ai player doesn't technically need parent ready, but don't want to override waiting for undo
+            elif self.ai_auto.active(1 - current_move.player) and not current_move.children:
+                self._do_aimove()
 
-    def _do_aimove(self, auto=False):
+    def _do_aimove(self):
         ts = self.train_settings
         while not self.board.current_move.analysis_ready:
             self.info.text = "Thinking..."
@@ -204,9 +169,9 @@ class EngineControls(GridLayout):
         sgfmoves = re.findall(r"([BW])\[([a-z]{2})\]", sgf)
         moves = [Move(player=Move.PLAYERS.index(p.upper()), sgfcoords=(mv, self.board_size)) for p, mv in sgfmoves]
         for move in moves:
-            self.board.play(move)
+            self.play(move)
         while not all(m.analysis for m in moves):
-            time.sleep(0.01)
+            time.sleep(0.05)
             self.info.text = f"{sum([1 if m.analysis else 0 for m in moves])}/{len(moves)} analyzed"
 
     # analysis thread
@@ -215,7 +180,8 @@ class EngineControls(GridLayout):
             while self.outstanding_analysis_queries:
                 self._send_analysis_query(self.outstanding_analysis_queries.pop(0))
             line = self.kata.stdout.readline()
-            print("KATA ANALYSIS RECEIVED:", line[:50])
+            if self.debug:
+                print("KATA ANALYSIS RECEIVED:", line[:50])
             self.board.store_analysis(json.loads(line))
             self.update_evaluation()
             self.redraw(include_board=False)
@@ -242,14 +208,14 @@ class EngineControls(GridLayout):
             "includeOwnership": True,
             "maxVisits": self.visits[fast][1],
         }
-        print("query", query)
+        if self.debug:
+            print("query", query)
         self._send_analysis_query(query)
         query.update(
             {"id": f"PASS_{move_id}", "maxVisits": self.visits[fast][0], "includeOwnership": False}
-        )  # TODO: merge?
+        )
         query["moves"] += [[move.bw_player(next_move=True), "pass"]]
         query["analyzeTurns"][0] += 1
-        print("pass-query", query)
         self._send_analysis_query(query)
 
 
diff --git a/katrain.kv b/katrain.kv
index d168454..4fbbda7 100644
--- a/katrain.kv
+++ b/katrain.kv
@@ -168,11 +168,11 @@
 			id: auto_undo
 			text: 'undo'
 			on_active: root.parent.board.redraw()
-		CheckBoxHint:
-			size_hint: 0.2, 0.5
-			text: 'lock\nai'
-			id: ai_lock
-			on_active: self.checkbox.disabled = hints.black.disabled = hints.white.disabled = ai_move.disabled = auto_undo.black.disabled = auto_undo.white.disabled = ai_auto.checkbox.disabled = True
+		BWCheckBoxHint:
+			size_hint: 0.166, 0.5
+			text: 'ai'
+			id: ai_auto
+			default_active: False
 		CheckBoxHint:
 			size_hint: 0.2, 0.5
 			id: ownership
@@ -189,19 +189,19 @@
 			on_press: root.action("aimove")
 		CheckBoxHint:
 			size_hint: 0.166, 0.5
-			text: 'auto\nmove'
-			id: ai_auto
-			default_active: False
+			text: 'fast'
+			id: ai_fast
+			default_active: True
 		CheckBoxHint:
 			size_hint: 0.166, 0.5
 			text: 'balance\nscore'
 			id: ai_balance
 			default_active: False
 		CheckBoxHint:
-			size_hint: 0.166, 0.5
-			text: 'fast'
-			id: ai_fast
-			default_active: True
+			size_hint: 0.2, 0.5
+			text: 'lock\nai'
+			id: ai_lock
+			on_active: self.checkbox.disabled = hints.black.disabled = hints.white.disabled = ai_move.disabled = auto_undo.black.disabled = auto_undo.white.disabled = ai_auto.checkbox.disabled = True
 	GridLayout:
 		cols: 2
 		rows: 1
diff --git a/katrain.py b/katrain.py
index e2a2da7..bfe1475 100644
--- a/katrain.py
+++ b/katrain.py
@@ -47,6 +47,12 @@ class BadukPanWidget(Widget):
     def on_touch_up(self, touch):
         if self.ghost_stone:
             self.engine.action("play", self.ghost_stone)
+        else:
+            xd, xp = self._find_closest(touch.x)
+            yd, yp = self._find_closest(touch.y)
+            stones_here = [m for m in self.engine.board.stones if m.coords == (xp,yp)]
+            if stones_here and max(yd, xd) < self.grid_size / 2:  # load old comment
+                self.engine.info.text = stones_here[-1].comment(sgf=True)
         self.ghost_stone = None
         self.redraw() # remove ghost