object time
Measure running time of a function/block
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- final def !=(arg0: Any): Boolean
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- final def ##: Int
- Definition Classes
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- final def ==(arg0: Any): Boolean
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- def apply[A](message: String)(f: => A): A
Executes a code block and measure the running time.
Executes a code block and measure the running time.
- A
The output type of code block.
- message
the log message.
- f
a code block to measure the running time.
- returns
the code block expression result.
- def apply[A](f: => A): A
Executes a code block and measure the running time.
Executes a code block and measure the running time.
- A
The output type of code block.
- f
a code block to measure the running time.
- returns
the code block expression result.
- final def asInstanceOf[T0]: T0
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- def clone(): AnyRef
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- protected[lang]
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- @throws(classOf[java.lang.CloneNotSupportedException]) @native()
- var echo: Boolean
Print out switch.
- final def eq(arg0: AnyRef): Boolean
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- def equals(arg0: AnyRef): Boolean
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- def finalize(): Unit
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- @throws(classOf[java.lang.Throwable])
- final def getClass(): Class[_ <: AnyRef]
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- @native()
- def hashCode(): Int
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- @native()
- final def isInstanceOf[T0]: Boolean
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- final def ne(arg0: AnyRef): Boolean
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- final def notify(): Unit
- Definition Classes
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- @native()
- final def notifyAll(): Unit
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- @native()
- def off(): Unit
Turn on printing out running time.
- def on(): Unit
Turn on printing out running time.
- final def synchronized[T0](arg0: => T0): T0
- Definition Classes
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- def toString(): String
- Definition Classes
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- final def wait(): Unit
- Definition Classes
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- @throws(classOf[java.lang.InterruptedException])
- final def wait(arg0: Long, arg1: Int): Unit
- Definition Classes
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- Annotations
- @throws(classOf[java.lang.InterruptedException])
- final def wait(arg0: Long): Unit
- Definition Classes
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- @throws(classOf[java.lang.InterruptedException]) @native()
Smile (Statistical Machine Intelligence and Learning Engine) is a fast and comprehensive machine learning, NLP, linear algebra, graph, interpolation, and visualization system in Java and Scala. With advanced data structures and algorithms, Smile delivers state-of-art performance.
Smile covers every aspect of machine learning, including classification, regression, clustering, association rule mining, feature selection, manifold learning, multidimensional scaling, genetic algorithms, missing value imputation, efficient nearest neighbor search, etc.