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Update scala model: 0.1.0

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GitOrigin-RevId: ce407fb957fc4df5b8d51328a51b4190eb612460
This commit is contained in:
Roman Vasiliev
2020-01-13 20:37:39 +00:00
committed by intellij-monorepo-bot
parent 5669893eb4
commit c1abea2ccc
2 changed files with 4 additions and 4 deletions
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plugins/stats-collector/completion-ranker/intellij.statsCollector.completionRanker.iml
+2 -2
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@@ -24,9 +24,9 @@
</orderEntry>
<orderEntry type="module-library">
<library name="completion-ranking-scala" type="repository">
<properties maven-id="org.jetbrains.intellij.deps.completion:completion-ranking-scala:0.0.1" />
<properties maven-id="org.jetbrains.intellij.deps.completion:completion-ranking-scala:0.1.0" />
<CLASSES>
<root url="jar://$MAVEN_REPOSITORY$/org/jetbrains/intellij/deps/completion/completion-ranking-scala/0.0.1/completion-ranking-scala-0.0.1.jar!/" />
<root url="jar://$MAVEN_REPOSITORY$/org/jetbrains/intellij/deps/completion/completion-ranking-scala/0.1.0/completion-ranking-scala-0.1.0.jar!/" />
</CLASSES>
<JAVADOC />
<SOURCES />
plugins/stats-collector/completion-ranker/src/com/jetbrains/completion/ranker/FallbackScalaMLRankingProvider.kt
+2 -2
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@@ -1,7 +1,7 @@
// Copyright 2000-2019 JetBrains s.r.o. Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
package com.jetbrains.completion.ranker
import com.completion.ranker.model.scala.MLWhiteBox
import com.completion.ranker.model.scala.MLGlassBox
import com.intellij.ide.plugins.PluginManager
import com.intellij.internal.ml.DecisionFunction
import com.intellij.internal.ml.ModelMetadata
@@ -13,7 +13,7 @@ import com.intellij.openapi.extensions.PluginId
class FallbackScalaMLRankingProvider : JarCompletionModelProvider("Scala", "scala_features"), WeakModelProvider {
override fun createModel(metadata: ModelMetadata): DecisionFunction {
return object : CompletionRankingModelBase(metadata) {
override fun predict(features: DoubleArray?): Double = MLWhiteBox.makePredict(features)
override fun predict(features: DoubleArray?): Double = MLGlassBox.makePredict(features)
}
}