pipeline

Do function pointers force an instruction pipeline to clear?

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On some processors an indirect branch will always clear at least part of the pipeline, because it will always mispredict. This is especially the case for in-order processors. For example, I ran some timings on the processor we develop for, comparing the overhead of an inline function call, versus a direct function call, versus an … Read more

Pipe complete array-objects instead of array items one at a time?

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Short answer: use unary array operator ,: ,$theArray | foreach{Write-Host $_} Long answer: there is one thing you should understand about @() operator: it always interpret its content as statement, even if content is just an expression. Consider this code: $a=”A”,’B’,’C’ $b=@($a;) $c=@($b;) I add explicit end of statement mark ; here, although PowerShell allows … Read more

“Piping” output from one function to another using Python infix syntax

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It is hard to implement this using the bitwise or operator because pandas.DataFrame implements it. If you don’t mind replacing | with >>, you can try this: import pandas as pd def select(df, *args): cols = [x for x in args] return df[cols] def rename(df, **kwargs): for name, value in kwargs.items(): df = df.rename(columns={‘%s’ % … Read more

How do you determine if WPF is using Hardware or Software Rendering?

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Check RenderCapability.Tier Graphics Rendering Tiers RenderCapability Class [UPDATE] RenderCapability.IsPixelShaderVersionSupported – Gets a value that indicates whether the specified pixel shader version is supported. RenderCapability.IsShaderEffectSoftwareRenderingSupported – Gets a value that indicates whether the system can render bitmap effects in software. RenderCapability.Tier – Gets a value that indicates the rendering tier for the current thread. RenderCapability.TierChanged – … Read more

How to properly pickle sklearn pipeline when using custom transformer

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I found a pretty straightforward solution. Assuming you are using Jupyter notebooks for training: Create a .py file where the custom transformer is defined and import it to the Jupyter notebook. This is the file custom_transformer.py from sklearn.pipeline import TransformerMixin class FilterOutBigValuesTransformer(TransformerMixin): def __init__(self): pass def fit(self, X, y=None): self.biggest_value = X.c1.max() return self def … Read more