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Implementing a system where, when it comes to the heavy mathematical lifting, I want to do as little as possible.
I'm aware that there are issues with memoisation with numpy objects, and as such implemented a lazy-key cache to avoid the whole "Premature optimisation" argument.
def magic(numpyarg,intarg): key = str(numpyarg)+str(intarg) try: ret = self._cache[key] return ret except: pass ... here be dragons ... self._cache[key]=value return value but since string conversion takes quite a while...
t=timeit.Timer("str(a)","import numpy;a=numpy.random.rand(10,10)") t.timeit(number=100000)/100000 = 0.00132s/call What do people suggest as being 'the better way' to do it?
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By explicitly declaring the "ndarray" data type, your array processing can be 1250x faster. This tutorial will show you how to speed up the processing of NumPy arrays using Cython. By explicitly specifying the data types of variables in Python, Cython can give drastic speed increases at runtime.
pandas provides a bunch of C or Cython optimized functions that can be faster than the NumPy equivalent function (e.g. reading text from text files). If you want to do mathematical operations like a dot product, calculating mean, and some more, pandas DataFrames are generally going to be slower than a NumPy array.
Only immutable types are hashable while mutable types like NumPy arrays are not hashable because they could change and break the lookup based on the hashing algorithm.
The Python code can't be faster than properly-coded C++ code since Numpy is coded in C, which is often slower than C++ since C++ can do more optimizations.
Borrowed from this answer... so really I guess this is a duplicate:
>>> import hashlib >>> import numpy >>> a = numpy.random.rand(10, 100) >>> b = a.view(numpy.uint8) >>> hashlib.sha1(b).hexdigest() '15c61fba5c969e5ed12cee619551881be908f11b' >>> t=timeit.Timer("hashlib.sha1(a.view(numpy.uint8)).hexdigest()", "import hashlib;import numpy;a=numpy.random.rand(10,10)") >>> t.timeit(number=10000)/10000 2.5790500640869139e-05 If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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