Examples fix
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@ -9,15 +9,10 @@ structures. In `kmath` performance depends on which particular context was used
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Let us consider following contexts:
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```kotlin
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// specialized nd-field for Double. It works as generic Double field as well
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val specializedField = NDField.real(intArrayOf(dim, dim))
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// automatically build context most suited for given type.
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val autoField = NDField.auto(intArrayOf(dim, dim), RealField)
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//A field implementing lazy computations. All elements are computed on-demand
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val lazyField = NDField.lazy(intArrayOf(dim, dim), RealField)
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val autoField = NDField.auto(RealField, dim, dim)
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// specialized nd-field for Double. It works as generic Double field as well
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val specializedField = NDField.real(dim, dim)
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//A generic boxing field. It should be used for objects, not primitives.
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val genericField = NDField.buffered(intArrayOf(dim, dim), RealField)
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```
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@ -32,7 +27,7 @@ to it `n = 1000` times.
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The code to run this looks like:
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```kotlin
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specializedField.run {
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var res = one
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var res: NDBuffer<Double> = one
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repeat(n) {
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res += 1.0
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}
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@ -93,7 +88,7 @@ In this case it completes in about `4x-5x` time due to boxing.
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The boxing field produced by
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```kotlin
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genericField.run {
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var res = one
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var res: NDBuffer<Double> = one
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repeat(n) {
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res += 1.0
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}
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