Map list to bin numbersquantilization (if that is a word)Map efficiently over duplicates in listGenerating a list of cubefree numbersIs there an equivalent to MATLAB's linspace?Convert a list of hexadecimal numbers to decimalTaking one list Mod a second listlist of items and group of alternative itemsHow find numbers in this list of inequalities?Selecting list entries with a True False index list of similar lengthReplace element in array by checking condition in another listAttempting to fill a table with the number of elements in each bin and make a table with the elements in the bins?
Is this food a bread or a loaf?
Can a planet have a different gravitational pull depending on its location in orbit around its sun?
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Map list to bin numbers
How can I add custom success page
Does it makes sense to buy a new cycle to learn riding?
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Doomsday-clock for my fantasy planet
What do the Banks children have against barley water?
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Map list to bin numbers
quantilization (if that is a word)Map efficiently over duplicates in listGenerating a list of cubefree numbersIs there an equivalent to MATLAB's linspace?Convert a list of hexadecimal numbers to decimalTaking one list Mod a second listlist of items and group of alternative itemsHow find numbers in this list of inequalities?Selecting list entries with a True False index list of similar lengthReplace element in array by checking condition in another listAttempting to fill a table with the number of elements in each bin and make a table with the elements in the bins?
$begingroup$
Does WL have the equivalent of Matlab's discretize or NumPy's digitize? I.e., a function that takes a length-N list and a list of bin edges and returns a length-N list of bin numbers, mapping each list item to its bin number?
list-manipulation data
$endgroup$
add a comment |
$begingroup$
Does WL have the equivalent of Matlab's discretize or NumPy's digitize? I.e., a function that takes a length-N list and a list of bin edges and returns a length-N list of bin numbers, mapping each list item to its bin number?
list-manipulation data
$endgroup$
$begingroup$
HistogramList
seems similar. This could also be done efficiently withGroupBy
and some easy littleCompile
-d selection determiner. Or maybe hit it first withSort
then write something that only checks the next bin up. Again, can be easilyCompile
-d.
$endgroup$
– b3m2a1
7 hours ago
$begingroup$
I need it to work like a map (in terms of the order of the items in the resulting list). Of course it is possible to write something ...
$endgroup$
– Alan
6 hours ago
$begingroup$
Related: 140577
$endgroup$
– Carl Woll
2 hours ago
1
$begingroup$
Did you tryBinCounts
? I guess it is what you need.
$endgroup$
– Rom38
1 hour ago
$begingroup$
@Rom38 You probably meantBinLists
, right?
$endgroup$
– Henrik Schumacher
42 mins ago
add a comment |
$begingroup$
Does WL have the equivalent of Matlab's discretize or NumPy's digitize? I.e., a function that takes a length-N list and a list of bin edges and returns a length-N list of bin numbers, mapping each list item to its bin number?
list-manipulation data
$endgroup$
Does WL have the equivalent of Matlab's discretize or NumPy's digitize? I.e., a function that takes a length-N list and a list of bin edges and returns a length-N list of bin numbers, mapping each list item to its bin number?
list-manipulation data
list-manipulation data
edited 1 hour ago
user64494
3,58411122
3,58411122
asked 7 hours ago
AlanAlan
6,6431125
6,6431125
$begingroup$
HistogramList
seems similar. This could also be done efficiently withGroupBy
and some easy littleCompile
-d selection determiner. Or maybe hit it first withSort
then write something that only checks the next bin up. Again, can be easilyCompile
-d.
$endgroup$
– b3m2a1
7 hours ago
$begingroup$
I need it to work like a map (in terms of the order of the items in the resulting list). Of course it is possible to write something ...
$endgroup$
– Alan
6 hours ago
$begingroup$
Related: 140577
$endgroup$
– Carl Woll
2 hours ago
1
$begingroup$
Did you tryBinCounts
? I guess it is what you need.
$endgroup$
– Rom38
1 hour ago
$begingroup$
@Rom38 You probably meantBinLists
, right?
$endgroup$
– Henrik Schumacher
42 mins ago
add a comment |
$begingroup$
HistogramList
seems similar. This could also be done efficiently withGroupBy
and some easy littleCompile
-d selection determiner. Or maybe hit it first withSort
then write something that only checks the next bin up. Again, can be easilyCompile
-d.
$endgroup$
– b3m2a1
7 hours ago
$begingroup$
I need it to work like a map (in terms of the order of the items in the resulting list). Of course it is possible to write something ...
$endgroup$
– Alan
6 hours ago
$begingroup$
Related: 140577
$endgroup$
– Carl Woll
2 hours ago
1
$begingroup$
Did you tryBinCounts
? I guess it is what you need.
$endgroup$
– Rom38
1 hour ago
$begingroup$
@Rom38 You probably meantBinLists
, right?
$endgroup$
– Henrik Schumacher
42 mins ago
$begingroup$
HistogramList
seems similar. This could also be done efficiently with GroupBy
and some easy little Compile
-d selection determiner. Or maybe hit it first with Sort
then write something that only checks the next bin up. Again, can be easily Compile
-d.$endgroup$
– b3m2a1
7 hours ago
$begingroup$
HistogramList
seems similar. This could also be done efficiently with GroupBy
and some easy little Compile
-d selection determiner. Or maybe hit it first with Sort
then write something that only checks the next bin up. Again, can be easily Compile
-d.$endgroup$
– b3m2a1
7 hours ago
$begingroup$
I need it to work like a map (in terms of the order of the items in the resulting list). Of course it is possible to write something ...
$endgroup$
– Alan
6 hours ago
$begingroup$
I need it to work like a map (in terms of the order of the items in the resulting list). Of course it is possible to write something ...
$endgroup$
– Alan
6 hours ago
$begingroup$
Related: 140577
$endgroup$
– Carl Woll
2 hours ago
$begingroup$
Related: 140577
$endgroup$
– Carl Woll
2 hours ago
1
1
$begingroup$
Did you try
BinCounts
? I guess it is what you need.$endgroup$
– Rom38
1 hour ago
$begingroup$
Did you try
BinCounts
? I guess it is what you need.$endgroup$
– Rom38
1 hour ago
$begingroup$
@Rom38 You probably meant
BinLists
, right?$endgroup$
– Henrik Schumacher
42 mins ago
$begingroup$
@Rom38 You probably meant
BinLists
, right?$endgroup$
– Henrik Schumacher
42 mins ago
add a comment |
3 Answers
3
active
oldest
votes
$begingroup$
This is a very quick-n-dirty, but may serve as a simple example.
This creates a piecewise function following the first definition in Matlab's discretize documentation, then applies that to the data.
disc[data_, edges_] := Module[e = Partition[edges, 2, 1], p, l,
l = Length@e;
Table[Piecewise[
Append[Table[i, e[[i, 1]] <= x < e[[i, 2]], i, l - 1]
, l,e[[l, 1]] <= x <= e[[l, 2]]]
, "NaN"]
, x, data]];
From the first example in the above referenced documentation:
data=1, 1, 2, 3, 6, 5, 8, 10, 4, 4;
edges=2, 4, 6, 8, 10;
disc[data,edges]
NaN,NaN,1,1,3,2,4,4,2,2
I'm sure there are more efficient/elegant solutions, and will revisit as time permits.
$endgroup$
add a comment |
$begingroup$
Here's a version based on Nearest
:
digitize[edges_] := DigitizeFunction[edges, Nearest[edges -> "Index"]]
digitize[data_, edges_] := digitize[edges][data]
DigitizeFunction[edges_, nf_NearestFunction][data_] := With[init = nf[data][[All, 1]],
init + UnitStep[data - edges[[init]]] - 1
]
For example:
SeedRandom[1]
data = RandomReal[10, 10]
digitize[data, 2, 4, 5, 7, 8]
8.17389, 1.1142, 7.89526, 1.87803, 2.41361, 0.657388, 5.42247, 2.31155, 3.96006, 7.00474
5, 0, 4, 0, 1, 0, 3, 1, 1, 4
Note that I broke up the definition of digitize
into two pieces, so that if you do this for multiple data sets with the same edges
list, you only need to compute the nearest function once.
$endgroup$
add a comment |
$begingroup$
You may also use Interpolation
with InterpolationOrder -> 0
. However, employing Nearest
as Carl Woll did will usually be much faster.
First, we prepare the interplating function.
m = 20;
binboundaries = Join[-1., Sort[RandomReal[-1, 1, m - 1]], 1.];
f = Interpolation[Transpose[binboundaries, Range[0, m]], InterpolationOrder -> 0];
Now you can apply it to lists of values as follows:
vals = RandomReal[-1, 1, 1000];
Round[f[vals]]
$endgroup$
add a comment |
Your Answer
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3 Answers
3
active
oldest
votes
3 Answers
3
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
This is a very quick-n-dirty, but may serve as a simple example.
This creates a piecewise function following the first definition in Matlab's discretize documentation, then applies that to the data.
disc[data_, edges_] := Module[e = Partition[edges, 2, 1], p, l,
l = Length@e;
Table[Piecewise[
Append[Table[i, e[[i, 1]] <= x < e[[i, 2]], i, l - 1]
, l,e[[l, 1]] <= x <= e[[l, 2]]]
, "NaN"]
, x, data]];
From the first example in the above referenced documentation:
data=1, 1, 2, 3, 6, 5, 8, 10, 4, 4;
edges=2, 4, 6, 8, 10;
disc[data,edges]
NaN,NaN,1,1,3,2,4,4,2,2
I'm sure there are more efficient/elegant solutions, and will revisit as time permits.
$endgroup$
add a comment |
$begingroup$
This is a very quick-n-dirty, but may serve as a simple example.
This creates a piecewise function following the first definition in Matlab's discretize documentation, then applies that to the data.
disc[data_, edges_] := Module[e = Partition[edges, 2, 1], p, l,
l = Length@e;
Table[Piecewise[
Append[Table[i, e[[i, 1]] <= x < e[[i, 2]], i, l - 1]
, l,e[[l, 1]] <= x <= e[[l, 2]]]
, "NaN"]
, x, data]];
From the first example in the above referenced documentation:
data=1, 1, 2, 3, 6, 5, 8, 10, 4, 4;
edges=2, 4, 6, 8, 10;
disc[data,edges]
NaN,NaN,1,1,3,2,4,4,2,2
I'm sure there are more efficient/elegant solutions, and will revisit as time permits.
$endgroup$
add a comment |
$begingroup$
This is a very quick-n-dirty, but may serve as a simple example.
This creates a piecewise function following the first definition in Matlab's discretize documentation, then applies that to the data.
disc[data_, edges_] := Module[e = Partition[edges, 2, 1], p, l,
l = Length@e;
Table[Piecewise[
Append[Table[i, e[[i, 1]] <= x < e[[i, 2]], i, l - 1]
, l,e[[l, 1]] <= x <= e[[l, 2]]]
, "NaN"]
, x, data]];
From the first example in the above referenced documentation:
data=1, 1, 2, 3, 6, 5, 8, 10, 4, 4;
edges=2, 4, 6, 8, 10;
disc[data,edges]
NaN,NaN,1,1,3,2,4,4,2,2
I'm sure there are more efficient/elegant solutions, and will revisit as time permits.
$endgroup$
This is a very quick-n-dirty, but may serve as a simple example.
This creates a piecewise function following the first definition in Matlab's discretize documentation, then applies that to the data.
disc[data_, edges_] := Module[e = Partition[edges, 2, 1], p, l,
l = Length@e;
Table[Piecewise[
Append[Table[i, e[[i, 1]] <= x < e[[i, 2]], i, l - 1]
, l,e[[l, 1]] <= x <= e[[l, 2]]]
, "NaN"]
, x, data]];
From the first example in the above referenced documentation:
data=1, 1, 2, 3, 6, 5, 8, 10, 4, 4;
edges=2, 4, 6, 8, 10;
disc[data,edges]
NaN,NaN,1,1,3,2,4,4,2,2
I'm sure there are more efficient/elegant solutions, and will revisit as time permits.
answered 5 hours ago
ciaociao
17.4k138109
17.4k138109
add a comment |
add a comment |
$begingroup$
Here's a version based on Nearest
:
digitize[edges_] := DigitizeFunction[edges, Nearest[edges -> "Index"]]
digitize[data_, edges_] := digitize[edges][data]
DigitizeFunction[edges_, nf_NearestFunction][data_] := With[init = nf[data][[All, 1]],
init + UnitStep[data - edges[[init]]] - 1
]
For example:
SeedRandom[1]
data = RandomReal[10, 10]
digitize[data, 2, 4, 5, 7, 8]
8.17389, 1.1142, 7.89526, 1.87803, 2.41361, 0.657388, 5.42247, 2.31155, 3.96006, 7.00474
5, 0, 4, 0, 1, 0, 3, 1, 1, 4
Note that I broke up the definition of digitize
into two pieces, so that if you do this for multiple data sets with the same edges
list, you only need to compute the nearest function once.
$endgroup$
add a comment |
$begingroup$
Here's a version based on Nearest
:
digitize[edges_] := DigitizeFunction[edges, Nearest[edges -> "Index"]]
digitize[data_, edges_] := digitize[edges][data]
DigitizeFunction[edges_, nf_NearestFunction][data_] := With[init = nf[data][[All, 1]],
init + UnitStep[data - edges[[init]]] - 1
]
For example:
SeedRandom[1]
data = RandomReal[10, 10]
digitize[data, 2, 4, 5, 7, 8]
8.17389, 1.1142, 7.89526, 1.87803, 2.41361, 0.657388, 5.42247, 2.31155, 3.96006, 7.00474
5, 0, 4, 0, 1, 0, 3, 1, 1, 4
Note that I broke up the definition of digitize
into two pieces, so that if you do this for multiple data sets with the same edges
list, you only need to compute the nearest function once.
$endgroup$
add a comment |
$begingroup$
Here's a version based on Nearest
:
digitize[edges_] := DigitizeFunction[edges, Nearest[edges -> "Index"]]
digitize[data_, edges_] := digitize[edges][data]
DigitizeFunction[edges_, nf_NearestFunction][data_] := With[init = nf[data][[All, 1]],
init + UnitStep[data - edges[[init]]] - 1
]
For example:
SeedRandom[1]
data = RandomReal[10, 10]
digitize[data, 2, 4, 5, 7, 8]
8.17389, 1.1142, 7.89526, 1.87803, 2.41361, 0.657388, 5.42247, 2.31155, 3.96006, 7.00474
5, 0, 4, 0, 1, 0, 3, 1, 1, 4
Note that I broke up the definition of digitize
into two pieces, so that if you do this for multiple data sets with the same edges
list, you only need to compute the nearest function once.
$endgroup$
Here's a version based on Nearest
:
digitize[edges_] := DigitizeFunction[edges, Nearest[edges -> "Index"]]
digitize[data_, edges_] := digitize[edges][data]
DigitizeFunction[edges_, nf_NearestFunction][data_] := With[init = nf[data][[All, 1]],
init + UnitStep[data - edges[[init]]] - 1
]
For example:
SeedRandom[1]
data = RandomReal[10, 10]
digitize[data, 2, 4, 5, 7, 8]
8.17389, 1.1142, 7.89526, 1.87803, 2.41361, 0.657388, 5.42247, 2.31155, 3.96006, 7.00474
5, 0, 4, 0, 1, 0, 3, 1, 1, 4
Note that I broke up the definition of digitize
into two pieces, so that if you do this for multiple data sets with the same edges
list, you only need to compute the nearest function once.
edited 2 hours ago
answered 3 hours ago
Carl WollCarl Woll
73k396189
73k396189
add a comment |
add a comment |
$begingroup$
You may also use Interpolation
with InterpolationOrder -> 0
. However, employing Nearest
as Carl Woll did will usually be much faster.
First, we prepare the interplating function.
m = 20;
binboundaries = Join[-1., Sort[RandomReal[-1, 1, m - 1]], 1.];
f = Interpolation[Transpose[binboundaries, Range[0, m]], InterpolationOrder -> 0];
Now you can apply it to lists of values as follows:
vals = RandomReal[-1, 1, 1000];
Round[f[vals]]
$endgroup$
add a comment |
$begingroup$
You may also use Interpolation
with InterpolationOrder -> 0
. However, employing Nearest
as Carl Woll did will usually be much faster.
First, we prepare the interplating function.
m = 20;
binboundaries = Join[-1., Sort[RandomReal[-1, 1, m - 1]], 1.];
f = Interpolation[Transpose[binboundaries, Range[0, m]], InterpolationOrder -> 0];
Now you can apply it to lists of values as follows:
vals = RandomReal[-1, 1, 1000];
Round[f[vals]]
$endgroup$
add a comment |
$begingroup$
You may also use Interpolation
with InterpolationOrder -> 0
. However, employing Nearest
as Carl Woll did will usually be much faster.
First, we prepare the interplating function.
m = 20;
binboundaries = Join[-1., Sort[RandomReal[-1, 1, m - 1]], 1.];
f = Interpolation[Transpose[binboundaries, Range[0, m]], InterpolationOrder -> 0];
Now you can apply it to lists of values as follows:
vals = RandomReal[-1, 1, 1000];
Round[f[vals]]
$endgroup$
You may also use Interpolation
with InterpolationOrder -> 0
. However, employing Nearest
as Carl Woll did will usually be much faster.
First, we prepare the interplating function.
m = 20;
binboundaries = Join[-1., Sort[RandomReal[-1, 1, m - 1]], 1.];
f = Interpolation[Transpose[binboundaries, Range[0, m]], InterpolationOrder -> 0];
Now you can apply it to lists of values as follows:
vals = RandomReal[-1, 1, 1000];
Round[f[vals]]
answered 32 mins ago
Henrik SchumacherHenrik Schumacher
59.5k582166
59.5k582166
add a comment |
add a comment |
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$begingroup$
HistogramList
seems similar. This could also be done efficiently withGroupBy
and some easy littleCompile
-d selection determiner. Or maybe hit it first withSort
then write something that only checks the next bin up. Again, can be easilyCompile
-d.$endgroup$
– b3m2a1
7 hours ago
$begingroup$
I need it to work like a map (in terms of the order of the items in the resulting list). Of course it is possible to write something ...
$endgroup$
– Alan
6 hours ago
$begingroup$
Related: 140577
$endgroup$
– Carl Woll
2 hours ago
1
$begingroup$
Did you try
BinCounts
? I guess it is what you need.$endgroup$
– Rom38
1 hour ago
$begingroup$
@Rom38 You probably meant
BinLists
, right?$endgroup$
– Henrik Schumacher
42 mins ago