Python: make dual vector dot-product more pythonic
I have this dot product, calculating weighted means, and is applied to two columns in a list:
The calculation is a running dot-product, ie the list of temperature samples is far larger than the list of weights, hence the correction of subtracting len(weights) at the end of the main loop.
This traverses the list of weights twice, which is inefficient and degrades performance. How could this be done in a more pythonic way?
I also have concerns about the main loop. Would this be considered more pythonic?:
Last edited by figaro; 11-18-2019 at 06:01 PM..
Reason: Emphasise the fact that the lists are not of the same length, ie the dot product calculates a running weighted mean.
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LEARN ABOUT MOJAVE
mpscnnconvolutionweightsandbiasesstate
MPSCNNConvolutionWeightsAndBiasesState(3) MetalPerformanceShaders.framework MPSCNNConvolutionWeightsAndBiasesState(3)NAME
MPSCNNConvolutionWeightsAndBiasesState
SYNOPSIS
#import <MPSCNNConvolution.h>
Inherits MPSState.
Instance Methods
(nonnull instancetype) - initWithWeights:biases:
(nonnull instancetype) - initWithDevice:cnnConvolutionDescriptor:
Class Methods
(nonnull instancetype) + temporaryCNNConvolutionWeightsAndBiasesStateWithCommandBuffer:cnnConvolutionDescriptor:
Properties
__nonnull id< MTLBuffer > weights
__nullable id< MTLBuffer > biases
Detailed Description
The MPSCNNConvolutionWeightsAndBiasesState is returned by exportWeightsAndBiasesWithCommandBuffer: method on MPSCNNConvolution object. This
is mainly used for GPU side weights/biases update process. During training, application can keep a copy of weights, velocity, momentum
MTLBuffers in its data source, update the weights (in-place or out of place) with gradients obtained from MPSCNNConvolutionGradientState
and call [MPSCNNConvolution reloadWeightsAndBiasesWithCommandBuffer] with resulting updated MTLBuffer. If application does not want to keep
a copy of weights/biases, it can call [MPSCNNConvolution exportWeightsAndBiasesWithCommandBuffer:] to get the current weights from
convolution itself, do the updated and call reloadWithCommandBuffer.
Method Documentation
- (nonnull instancetype) initWithDevice: (__nonnull id< MTLDevice >) device(MPSCNNConvolutionDescriptor *__nonnull) descriptor
- (nonnull instancetype) initWithWeights: (__nonnull id< MTLBuffer >) weights(__nullable id< MTLBuffer >) biases
+ (nonnull instancetype) temporaryCNNConvolutionWeightsAndBiasesStateWithCommandBuffer: (__nonnull id< MTLCommandBuffer >)
commandBuffer(MPSCNNConvolutionDescriptor *__nonnull) descriptor
Property Documentation
- biases [read], [nonatomic], [assign]
A buffer that contains the biases. Each value is float and there are ouputFeatureChannels values.
- weights [read], [nonatomic], [assign]
A buffer that contains the weights. Each value in the buffer is a float. The layout of the weights with respect to the weights is the same
as the weights layout provided by data source i.e. it can be interpreted as 4D array
weights[outputFeatureChannels][kernelHeight][kernelWidth][inputFeatureChannels/groups]
Author
Generated automatically by Doxygen for MetalPerformanceShaders.framework from the source code.
Version MetalPerformanceShaders-100 Thu Feb 8 2018 MPSCNNConvolutionWeightsAndBiasesState(3)