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@@ -21,9 +21,9 @@ but has since grown to include a wide range of features, including:
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## Animated Screenshots
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| Analyze games | Play against an AI Teacher |
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| Play against an AI Teacher | | Analyze games |
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| ------------- | ------------- |
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|  |  |
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|  |  |
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# Documentation
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@@ -58,27 +58,23 @@ on the board or not output details for them in SGFs,you can do so under 'Configu
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This section describes the available AIs, with strength based on their current OGS rankings using the default settings.
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* Recommended options for serious play include:
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* **[9p+]** **KataGo** is full KataGo, above professional level.
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* **[9p+]** **KataGo** is full KataGo, above professional level. The analysis and feedback given is always based on this full strength KataGo AI.
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* **[~3k]** **ScoreLoss** is KataGo analyzing as usual, but
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choosing from potential moves depending on the expected score loss, leading to a varied style with mostly small mistakes.
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* **[~4d]** **Policy** uses the top move from the policy network (it's 'shape sense' without reading).
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* **[~5k]**: **Policy Weighted** picks a random move weighted by the policy, leading to a varied style with mostly small mistakes, and occasional blunders due to a lack of reading.
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* **[~8k]**: **Blinded Policy** picks a number of moves at random and play the best move among them, being effectively 'blind' to all moves not picked.
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The setting `pick_override` determines the minimum value at which this process is bypassed to play the best move instead, preventing obvious blunders.
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* Options that are more on the 'fun and experimenta' side include:
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* **KataJigo** is KataGo aggressively making weaker moves, attempting to win by 0.5 points.
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* **[~5k]** **Policy Weighted** picks a random move weighted by the policy, leading to a varied style with mostly small mistakes, and occasional blunders due to a lack of reading.
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* **[~8k]** **Blinded Policy** picks a number of moves at random and play the best move among them, being effectively 'blind' to part of the board each turn.
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* Options that are more on the 'fun and experimental' side include:
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* Variants of **Blinded Policy**, which use the same basic strategy, but with a twist.:
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* **[~5k]**: **Local Style** will consider mostly moves close to the last move.
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* **[~8k]**: **Tenuki Style** will consider mostly moves away from the last move.
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* **[~8k]**: **Influential Style** is biased towards 4th+ line moves, with every line below that
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reducing both the chance of considering the move and the policy value.
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* **[~5k]**: **P:Territory** is biased in the opposite way, towards 1-3rd line moves.
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* **[~5k]** **Local Style** will consider mostly moves close to the last move.
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* **[~8k]** **Tenuki Style** will consider mostly moves away from the last move.
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* **[~8k]** **Influential Style** will consider mostly 4th+ line moves, leading to a center-oriented style.
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* **[~5k]** **Territory Style** is biased in the opposite way, towards 1-3rd line moves.
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* **KataJigo** is KataGo aggressively making weaker moves, attempting to win by 0.5 points.
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The Engine based AIs (KataGo, ScoreLoss, KataJigo) are affected by both the model and choice of visits and maximum time,
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while the policy net based AIs are affected by the choice of model file, but work identically with 1 visit.
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Regardless of the AI chosen, the analysis and feedback given is always based on the full KataGo AI.
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### Analysis
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AI moves, teaching mode and timers are suspended in analysis mode,
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@@ -532,6 +532,7 @@
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markup: root.markup
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padding: 5, 5
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font_size: NOTES_FONT_SIZE
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line_height: root.line_height
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color: TEXT_COLOR
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size_hint_y: None
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height: self.texture_size[1]
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@@ -645,6 +646,7 @@
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background_color: DARKRED
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ScrollableLabel:
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id: info
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line_height: 1.25
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markup: True
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on_ref_press: root.katrain.board_gui.show_pv_from_comments(args[1])
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size_hint: 1, 1
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@@ -485,6 +485,7 @@ class ScrollableLabel(ScrollView, BackgroundMixin):
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__events__ = ["on_ref_press"]
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outline_color = ListProperty([0, 0, 0, 0]) # mixin not working for some reason
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text = StringProperty("")
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line_height = NumericProperty(1)
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markup = BooleanProperty(False)
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def on_ref_press(self, ref):
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@@ -201,10 +201,10 @@ msgid "Info:PV"
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msgstr "변화도: {pv}"
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msgid "Info:policy rank"
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msgstr "정책망이 고려하는 #{rank}번째 수입니다({probability:.2%})."
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msgstr "정책망이 고려하는 #{rank}번째 수입니다 ({probability:.2%})."
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msgid "Info:policy best"
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msgstr "정책망의 일감은 {move}입니다({probability:.1%})."
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msgstr "정책망의 일감은 {move}입니다 ({probability:.1%})."
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msgid "Info:teaching undo"
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msgstr "지도대국이므로 자동으로 물렀습니다."
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