![]() ![]() Setup='''labels= from _main_ import entrop圓''',ĭ = timeit. Setup='''labels= from _main_ import entropy2''',Ĭ = timeit.repeat(stmt='''entrop圓(labels)''', Setup='''labels= from _main_ import entropy1''',ī = timeit.repeat(stmt='''entropy2(labels)''', Timeit operations: repeat_number = 1000000Ī = timeit.repeat(stmt='''entropy1(labels)''', Return -(norm_counts * np.log(norm_counts)/np.log(base)).sum() Return -(vc * np.log(vc)/np.log(base)).sum() Vc = pd.Series(labels).value_counts(normalize=True, sort=False) """ Computes entropy of label distribution. Value,counts = np.unique(labels, return_counts=True) First is the presence of the symbol log s. ) There are several things worth noting about this equation. ![]() This conception of entropy led to the development of modern statistical thermodynamics. This allows us to consider entropy from the perspective of the probabilities of different configurations of the constituent interacting particles in an ensemble. Internal interactions between various subsystems give multiple entropy changes. If the sample is completely homogeneous the entropy is zero and if the sample is an equally divided it has entropy of one 1. Entropy is generally defined as the degree of randomness of a macroscopic system. ID3 algorithm uses entropy to calculate the homogeneity of a sample. This question is specifically asking about the "Fastest" way but I only see times on one answer so I'll post a comparison of using scipy and numpy to the original poster's entropy2 answer with slight alterations.įour different approaches: (1) scipy/numpy, (2) numpy/math, (3) pandas/numpy, (4) numpy import numpy as np This is the quantity that he called entropy, and it is represented by H in the following formula: H p1 log s (1/ p1) + p2 log s (1/ p2) + + pk log s (1/ pk ). In this chapter we introduce the statistical definition of entropy as formulated by Boltzmann. Hence, we define a new state function to explain the spontaneity of a process. Gupta answer is good but could be condensed. ![]()
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