You can Use Float.intBitsToFloat() and Float.floatToIntBits() to convert them to and from primitive float values. If you can live with truncated precision (as opposed to rounding) the conversion should be possible to implement with just a few bit shifts. I have now put a little more effort into it and it turned out not quite … Read more
As indicated in comments, real*8 isn’t standard Fortran. This FAQ entry has more details. That said, once we’re into the non-standard realm… The 8 refers to the number of bytes that the data type uses. So a 32-bit integer is integer*4 along the same lines. (But is also non-standard.) A quick search found this guide … Read more
Comparison with astype(int) Tentatively convert your column to int and test with np.array_equal: np.array_equal(df.v, df.v.astype(int)) True float.is_integer You can use this python function in conjunction with an apply: df.v.apply(float.is_integer).all() True Or, using python’s all in a generator comprehension, for space efficiency: all(x.is_integer() for x in df.v) True
Consider sklearn.dummy.DummyClassifier(strategy=’uniform’) which is a classifier that make random guesses (a.k.a bad classifier). We can view DummyClassifier as a benchmark to beat, now let’s see it’s f1-score. In a binary classification problem, with balanced dataset: 6198 total sample, 3099 samples labelled as 0 and 3099 samples labelled as 1, f1-score is 0.5 for both classes, … Read more
You have a 19-digit literal, but double usually has 15-17 digit precision. As a result, you can get a small relative error (when converting to double), but big enough (in the context of sine calculation) absolute error. Actually, different implementations of the standard library have differences in treating such large numbers. For example, in my … Read more
For “more precise”: this recipe in the Python Cookbook has summation algorithms which keep the full precision (by keeping track of the subtotals). Code is in Python but even if you don’t know Python it’s clear enough to adapt to any other language. All the details are given in this paper.
recall_average = recall_score(Y_test, y_predict, average=”binary”, pos_label=”neg”) Use “neg” or “pos” as pos_label and this error won’t raise again.
I was looking for this too, and apart from this question, I also found this discussion regarding a feature request that led to the addition of closeTo. With that you can specify a value and a +/- delta, so basically it lets you specify the precision with which to check the result. percentage.should.be.closeTo(6.666, 0.001); or … Read more
Historical note: the comment thread below may refer to first and second implementations. I swapped the order in September 2017 since leading with a buggy implementation caused confusion. If you want something that maps “0.1e-100” to 101, then you can try something like function decimalPlaces(n) { // Make sure it is a number and use … Read more