sh managed to get to score 1.0 returns

Author: drblallo

environment.fish

@@ -0,0 +1,4 @@
+source ../.venv/bin/activate.fish
+set -x LD_LIBRARY_PATH (realpath ../llvm-install-release/lib):$LD_LIBRARY_PATH
+set -x LD_LIBRARY_PATH (realpath ../llvm-install-debug/lib):$LD_LIBRARY_PATH
+set -x PATH (realpath ../llvm-install-release/bin/):$PATH

environment.sh

@@ -1,5 +1,5 @@
 DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
-source $DIR/../.venv/local/bin/activate
+source $DIR/../.venv/bin/activate
 export LD_LIBRARY_PATH="$DIR/../llvm-install-release/lib/":$LD_LIBRARY_PATH
 export LD_LIBRARY_PATH="$DIR/../llvm-install-debug/lib/":$LD_LIBRARY_PATH
 export PATH="$DIR/../llvm-install-release/bin/":$PATH

lib/lsp/src/LSP.cpp

@@ -5,7 +5,7 @@ Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at
 
-   http://www.apache.org/licenses/LICENSE-2.0
+	 http://www.apache.org/licenses/LICENSE-2.0
 
 Unless required by applicable law or agreed to in writing, software
 distributed under the License is distributed on an "AS IS" BASIS,

stdlib/machine_learning.rl

@@ -0,0 +1,43 @@
+#
+#Copyright 2024 Massimo Fioravanti
+#
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
+#
+import action
+import collections.vector
+import string
+
+ent<T> Hidden:
+    T value
+
+    fun assign(T content):
+        self.value = content
+
+fun<T> write_in_observation_tensor(Hidden<T> obj, Vector<Float> output, Int index):
+    return
+
+fun<T> size_as_observation_tensor(Hidden<T> obj) -> Int:
+    return 0 
+
+fun<T> append_to_vector(Hidden<T> to_add, Vector<Byte> output):
+    append_to_byte_vector(to_add.value, output)
+
+fun<T> parse_from_vector(Hidden<T> to_add, Vector<Byte> output, Int index) -> Bool:
+    return from_byte_vector(to_add.value, output, index)
+
+fun<T> append_to_string(Hidden<T> to_add, String output):
+    to_string(to_add.value, output)
+
+fun<T> parse_string(Hidden<T> to_add, String input, Int index) -> Bool:
+    return from_string(to_add.value, input, index) 
+

stdlib/string.rl

@@ -229,6 +229,9 @@ fun<T> _to_string_impl(T to_add, String output):
         else:
             output.append('}')
 
+fun<T> to_string(T to_stringyfi, String output):
+    _to_string_impl(to_stringyfi, output)
+
 fun<T> to_string(T to_stringyfi) -> String:
     let to_return : String
     _to_string_impl(to_stringyfi, to_return)
@@ -386,3 +389,6 @@ fun<T> _parse_string_impl(T result, String buffer, Int index) -> Bool:
 fun<T> from_string(T result, String buffer) -> Bool:
     let index = 0
     return _parse_string_impl(result, buffer, index)
+
+fun<T> from_string(T result, String buffer, Int index) -> Bool:
+    return _parse_string_impl(result, buffer, index)

tool/rlc/src/Main.cpp

@@ -212,6 +212,7 @@ static cl::opt<bool> Optimize(
 		cl::desc("Optimize"),
 		cl::callback([](const bool &value) {
 			emitPreconditionChecks.setInitialValue(!value);
+			emitBoundChecks.setInitialValue(!value);
 		}),
 		cl::init(false),
 		cl::cat(astDumperCategory));

tool/rlc/test/examples/space_hulk/board.rl

@@ -3,11 +3,13 @@
 import unit 
 import bounded_arg
 import collections.vector
+import machine_learning
 
 using UnitArgType = BInt<0, 10>
 
+
 ent Board:
-  BInt<0, 2>[29][28] map
+  Hidden<BInt<0, 2>[29][28]> map
   HiddenInformation<BInt<0, 5>> command_points
   Vector<Unit> units
   Bool is_done
@@ -38,7 +40,7 @@ ent Board:
         while x != 29:
           if !(self.get_index_of_unit_at(x, y) is Nothing):
             to_print.append('o')
-          else if self.map[y][x] == 1:
+          else if self.map.value[y][x] == 1:
             to_print.append('X')
           else:
             to_print.append(' ')
@@ -71,7 +73,7 @@ ent Board:
         return false 
       iter = iter + 1
 
-    return self.map[y][x] == 0
+    return self.map.value[y][x] == 0
 
   fun unit_id_is_valid(Int unit_id) -> Bool:
     return unit_id >= 0 and unit_id < 10 and unit_id < self.units.size()
@@ -202,7 +204,7 @@ fun make_board() -> Board:
       let y = 28
       while y != 0:
           y = y - 1
-          board.map[y][x] = copy[y][x]
+          board.map.value[y][x] = copy[y][x]
 
   board.command_points.content = 0
   board.command_points.owner = 1