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for example I got many sub-arrays by splitting one array A based on list B:
A = np.array([[1,1,1],
[2,2,2],
[2,3,4],
[5,8,10],
[5,9,9],
[7,9,6],
[1,1,1],
[2,2,2],
[9,2,4],
[9,3,6],
[10,3,3],
[11,2,2]])
B = np.array([5,7])
C = np.split(A,B.cumsum()[:-1])
>>>print(C)
>>>array([[1,1,1],
[1,2,2],
[2,3,4],
[5,8,10],
[5,9,9]]),
array([[7,9,6],
[1,1,1],
[2,2,2],
[9,2,4],
[9,3,6],
[10,3,3],
[11,2,2]])
How can I find get the rows only appeared once in all the sub-arrays (delete those who appeared twice)? so that I can get the result like: (because [1,1,1] and [2,2,2] appeared twice in C )
>>>array([[2,3,4],
[5,8,10],
[5,9,9]]),
array([[7,9,6],
[9,2,4],
[9,3,6],
[10,3,3],
[11,2,2]])
You can use np.unique to identify the duplicates:
_, i, c = np.unique(A, axis=0, return_index=True, return_counts=True)
idx = np.isin(np.arange(len(A)), i[c==1])
out = [a[i] for a,i in zip(np.split(A, B.cumsum()[:-1]),
np.split(idx, B.cumsum()[:-1]))]
output:
[array([[ 2, 3, 4],
[ 5, 8, 10],
[ 5, 9, 9]]),
array([[ 7, 9, 6],
[ 9, 2, 4],
[ 9, 3, 6],
[10, 3, 3],
[11, 2, 2]])]
I am writing a very basic program with observations not exceeding 20 values (X1 is the original dateset).
X1_test=X1_df.iloc[0:20,]
from sklearn.cluster import AgglomerativeClustering
ag= AgglomerativeClustering(n_clusters=6, affinity= 'euclidean', linkage='ward', compute_full_tree= True, compute_distances=True)
ag.fit(X1_test)
When I run the attribute ag.chilren_ the values come as
array([[10, 13],
[ 1, 6],
[16, 18],
[ 2, 19],
[ 4, 20],
[ 8, 15],
[12, 23],
[14, 21],
[ 0, 17],
[ 9, 26],
[22, 27],
[11, 24],
[ 5, 29],
[ 7, 25],
[ 3, 28],
[30, 31],
[32, 35],
[33, 36],
[34, 37]], dtype=int64)
how come values in this output are coming more than 20 since i have only 20 observations?
Please help
According to what I can understand from reading Scikit-learn's documentation, each array represents a node that has two values. If the value is smaller than your sample size, it represents a leaf and you can therefore consider its value to be the sample index. But if the value is bigger or equal to your sample size than this is not a leaf, it is a different node (that merged earlier). Which node is it? The node that is stored in index [value - n_samples] in the children_ attribute.
So for example, if your sample size is 20 and you have a node that merges 3 with 28, you can understand that 3 is the leaf of your third sample and 28 is the node of children_[8] (because 28-20=8). So it will be the node of [14, 21] in your case.
I need to multiply an array (NIR) with a scalar (f) but leaving some values that meet a certain condition intact.
I tried the following:
NIR_f = np.multiply(NIR,f,where=NIR!=-28672.0)
To check I made:
i,j=1119,753
NIR[i][j],NIR_f[i][j]
and I got this:
(-28672.0, 10058.0)
It is assumed that both results should be the same! In that position the condition is not met, therefore the value should remain intact.
Am I using the "where" option wrongly?
Without your array, or a smaller substitute, I can't exactly replicate your problem. But there are potentially 2 issues
float testing is not exact, so it might be matching one -28672.0, and not another.
the remain intact assumption is tricky. leave the value in the output alone, but what was it originally, 0's or NIR values.
Using an integer array to avoid the float issue:
In [20]: arr = np.arange(12).reshape(3,4)
In [21]: arr
Out[21]:
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
In [22]: np.multiply(arr, 10, where=arr!=10)
Out[22]:
array([[ 0, 10, 20, 30],
[ 40, 50, 60, 70],
[ 80, 90, 481036337249, 110]])
In [24]: np.multiply(arr, 10, where=arr!=10)
Out[24]:
array([[ 0, 10, 20, 30],
[ 40, 50, 60, 70],
[ 80, 90, 0, 110]])
arr[2,2] is random. In effect it started with a np.empty array of the right shape and dtype, and filled all values but that one with the multiplication. To use where correctly we need to specify an out parameter as well.
In [25]: out = np.full(arr.shape,-1)
In [26]: out
Out[26]:
array([[-1, -1, -1, -1],
[-1, -1, -1, -1],
[-1, -1, -1, -1]])
In [27]: np.multiply(arr, 10, where=arr!=10, out=out)
Out[27]:
array([[ 0, 10, 20, 30],
[ 40, 50, 60, 70],
[ 80, 90, -1, 110]])
The issue of inexact floats comes up often enough that I won't try to illustrate that.
I have two piece codes. The first one is:
A = np.arange(3*4*3).reshape(3, 4, 3)
P = np.arange(1, 4)
A[:, 1:, :] = np.einsum('j, ijk->ijk', P, A[:, 1:, :])
and the result A is :
array([[[ 0, 1, 2],
[ 6, 8, 10],
[ 18, 21, 24],
[ 36, 40, 44]],
[[ 12, 13, 14],
[ 30, 32, 34],
[ 54, 57, 60],
[ 84, 88, 92]],
[[ 24, 25, 26],
[ 54, 56, 58],
[ 90, 93, 96],
[132, 136, 140]]])
The second one is:
A = np.arange(3*4*3).reshape(3, 4, 3)
P = np.arange(1, 4)
np.einsum('j, ijk->ijk', P, A[:, 1:, :], out=A[:,1:,:])
and the result A is :
array([[[ 0, 1, 2],
[ 0, 0, 0],
[ 0, 0, 0],
[ 0, 0, 0]],
[[12, 13, 14],
[ 0, 0, 0],
[ 0, 0, 0],
[ 0, 0, 0]],
[[24, 25, 26],
[ 0, 0, 0],
[ 0, 0, 0],
[ 0, 0, 0]]])
So the result is different. Here I want to use out to save memory. Is it a bug in numpy.einsum? Or I missed something?
By the way, my numpy version is 1.13.3.
I haven't used this new out parameter before, but have worked with einsum in the past, and have a general idea of how it works (or at least used to).
It looks to me like it initializes the out array to zero before the start of iteration. That would account for all the 0s in the A[:,1:,:] block. If instead I initial separate out array, the desired values are inserted
In [471]: B = np.ones((3,4,3),int)
In [472]: np.einsum('j, ijk->ijk', P, A[:, 1:, :], out=B[:,1:,:])
Out[472]:
array([[[ 3, 4, 5],
[ 12, 14, 16],
[ 27, 30, 33]],
[[ 15, 16, 17],
[ 36, 38, 40],
[ 63, 66, 69]],
[[ 27, 28, 29],
[ 60, 62, 64],
[ 99, 102, 105]]])
In [473]: B
Out[473]:
array([[[ 1, 1, 1],
[ 3, 4, 5],
[ 12, 14, 16],
[ 27, 30, 33]],
[[ 1, 1, 1],
[ 15, 16, 17],
[ 36, 38, 40],
[ 63, 66, 69]],
[[ 1, 1, 1],
[ 27, 28, 29],
[ 60, 62, 64],
[ 99, 102, 105]]])
The Python portion of einsum doesn't tell me much, except how it decides to pass the out array to the c portion, (as one of the list of tmp_operands):
c_einsum(einsum_str, *tmp_operands, **einsum_kwargs)
I know that it sets up a c-api equivalent of np.nditer, using the str to define the axes and iterations.
It iterates something like this section in the iteration tutorial:
https://docs.scipy.org/doc/numpy-1.13.0/reference/arrays.nditer.html#reduction-iteration
Notice in particular the it.reset() step. That sets the out buffer to 0 prior to iterating. It then iterates over the elements of input arrays and the output array, writing the calculation values to the output element. Since it is doing a sum of products (e.g. out[:] += ...), it has to start with a clean slate.
I'm guessing a bit as to what is actually going on, but it seems logical to me that it should zero out the output buffer to start with. If that array is the same as one of the inputs, that will end up messing with the calculation.
So I don't think this approach will work and save you memory. It needs a clean buffer to accumulate the results in. Once that's done it, or you, can write the values back into A. But given the nature of a dot like product, you can't use the same array for input and for output.
In [476]: A[:,1:,:] = np.einsum('j, ijk->ijk', P, A[:, 1:, :])
In [477]: A
Out[477]:
array([[[ 0, 1, 2],
[ 3, 4, 5],
[ 12, 14, 16],
[ 27, 30, 33]],
....)
In the C source code for einsum, there is a section that will take the array specified by out and do some zero-setting.
But in the Python source code for example, there are execution paths that call the tensordot function before ever descending the arguments to call c_einsum.
This means that some operations might be pre-computed (thus modifying your array A on some contraction passes) with tensordot, before any sub-array is ever set to zero by the zero-setter inside the C code for einsum.
Another way to put it is: on each pass at doing the next contraction operations, NumPy has many choices available to it. To use tensordot directly without getting into the C-level einsum code just yet? Or to prepare the arguments and pass to the C level (which will involve over-writing some sub-view of the output array with all zeros)? Or to re-order the operations and repeat the check?
Depending on the order it chooses for these optimizations, you can end up with unexpected all-zeros sub-arrays.
Best bet is to not try to be this clever and use the same array for the output. You say it is because you want to save memory. Yes, in some special cases an einsum operation might be do-able in-place. But it does not currently detect if this is the case and attempt to avoid the zero-setting.
And in a huge number of cases, over-writing into one of the input arrays during the middle of the overall operation would cause many problems, much like trying to append to a list you are directly looping over, etc.
Assume we have an array with NxMxD shape. I want to get a list with D NxM arrays.
The correct way of doing it would be:
np.dsplit(myarray, D)
However, this returns D NxMx1 arrays.
I can achieve the desired result by doing something like:
[myarray[..., i] for i in range(D)]
Or:
[np.squeeze(subarray) for subarray in np.dsplit(myarray, D)]
However, I feel like it is a bit redundant to need to perform an additional operation. Am I missing any numpy function that returns the desired result?
Try D.swapaxes(1,2).swapaxes(1,0)
>>>import numpy as np
>>>a = np.arange(24).reshape(2,3,4)
>>>a
array([[[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]],
[[12, 13, 14, 15],
[16, 17, 18, 19],
[20, 21, 22, 23]]])
>>>[a[:,:,i] for i in range(4)]
[array([[ 0, 4, 8],
[12, 16, 20]]),
array([[ 1, 5, 9],
[13, 17, 21]]),
array([[ 2, 6, 10],
[14, 18, 22]]),
array([[ 3, 7, 11],
[15, 19, 23]])]
>>>a.swapaxes(1,2).swapaxes(1,0)
array([[[ 0, 4, 8],
[12, 16, 20]],
[[ 1, 5, 9],
[13, 17, 21]],
[[ 2, 6, 10],
[14, 18, 22]],
[[ 3, 7, 11],
[15, 19, 23]]])
Edit: As pointed out by ajcr (thanks again), the transpose command is more convenient since the two swaps can be done in one step by using
D.transpose(2,0,1)
np.dsplit uses np.array_split, the core of which is:
sub_arys = []
sary = _nx.swapaxes(ary, axis, 0)
for i in range(Nsections):
st = div_points[i]; end = div_points[i+1]
sub_arys.append(_nx.swapaxes(sary[st:end], axis, 0))
with axis=-1, this is equivalent to:
[x[...,i:(i+1)] for i in np.arange(x.shape[-1])] # or
[x[...,[i]] for i in np.arange(x.shape[-1])]
which accounts for the singleton dimension.
So there's nothing wrong or inefficient about your
[x[...,i] for i in np.arange(x.shape[-1])]
Actually in quick time tests, any use of dsplit is slow. It's generality costs. So adding squeeze is relatively cheap.
But by accepting the other answer, it looks like you are really looking for an array of the correct shape, rather than a list of arrays. For many operations that makes sense. split is more useful when the subarrays have more than one 'row' along the split axis, or even an uneven number of 'rows'.