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Why do enum permissions often have 0, 1, 2, 4 values?
Why are people always using enum values like 0, 1, 2, 4, 8 and not 0, 1, 2, 3, 4 ?
7 Answers
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Can someone give an example of cosine similarity, in a very simple, graphical way?
... want to know how similar these texts are, purely in terms of word counts (and ignoring word order). We begin by making a list of the words from both texts:
me Julie loves Linda than more likes Jane
Now we count the number of times each of these words appears in each text:
me 2 2
Jane ...
An efficient way to transpose a file in Bash
... more explicit variable names will help answer some of the questions below and generally clarify what the script is doing. It also uses tabs as the separator which the OP had originally asked for so it'd handle empty fields and it coincidentally pretties-up the output a bit for this particular case....
Remove last item from array
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@PrithvirajMitra You want to remove 1 and 0? So the array ==[2] ?
– Anton
Oct 23 '13 at 14:40
3
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Pandas groupby: How to get a union of strings
...065 This
1 2 0.301084 is
2 3 0.463468 a
3 4 0.643961 random
4 1 0.866521 string
5 2 0.120737 !
In [6]: df.dtypes
Out[6]:
A int64
B float64
C object
dtype: object
When you apply your own function, there is not automatic exclusions of non-numeric columns...
Too many 'if' statements?
... it's ugly, excessive or a number of other things. I've looked at formulas and attempted to write a few solutions, but I end up with a similar amount of statements.
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How to access the ith column of a NumPy multidimensional array?
... reference is reflected in the original array.
– harmands
Oct 18 '16 at 14:21
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How to extract the decision rules from scikit-learn decision-tree?
...e output for a tree that is trying to return its input, a number between 0 and 10.
def tree(f0):
if f0 <= 6.0:
if f0 <= 1.5:
return [[ 0.]]
else: # if f0 > 1.5
if f0 <= 4.5:
if f0 <= 3.5:
return [[ 3.]]
else: # if f0 > 3.5
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pandas dataframe columns scaling with sklearn
I have a pandas dataframe with mixed type columns, and I'd like to apply sklearn's min_max_scaler to some of the columns. Ideally, I'd like to do these transformations in place, but haven't figured out a way to do that yet. I've written the following code that works:
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Fast stable sorting algorithm implementation in javascript
...oking to sort an array of about 200-300 objects, sorting on a specific key and a given order (asc/desc). The order of results must be consistent and stable.
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