vectorize | Automatic generation of vectorized code using C
kandi X-RAY | vectorize Summary
kandi X-RAY | vectorize Summary
The core of vectorize is the apply function. It is similar to the STL algorithm transform. Its function signature is:. The trick is that f is an expression whose type encodes the computation. I'll call it the "computation kernel." For example, sqrt(_x) doesn't compute anything, it just generates a type that knows how to compute the square root of vectors and scalars. The apply function knows how to process vectors of data using this computation kernel, and how to handle the remainder as scalars if the size isn't evenly divisible by the vector width.
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Lines of Code : 29License : Non-SPDX (Apache License 2.0)
Copydef _vectorize_then_blockify(self, matrix):
"""Shape batch matrix to batch vector, then blockify trailing dimensions."""
# Suppose
# matrix.shape = [m0, m1, m2, m3],
# and matrix is a matrix because the final two dimensions are matr 
License : No LicenseCopydef vectorize_stories(data, word2idx, story_maxlen, query_maxlen):
inputs, queries, answers = [], [], []
for story, query, answer in data:
inputs.append([[word2idx[w] for w in s] for s in story])
queries.append([word2idx[w] for w in query def vectorize(Text): return TfidfVectorizer().fit_transform(Text).toarray() Community Discussions
Trending Discussions on vectorize
QUESTION
Good day, everyone.
I want to have two separate TensorFlow models (f and g) and train both of them on the loss of f(g(x)). However, I want to use them separately, like g(x) or f(e), where e is an embedded vector but received not from g.
For example, the classical way to create the model with embedding looks like this:
...ANSWER
Answered 2021-Jun-15 at 10:53This can be achieved by weight sharing or shared layers. To share layers in different models in keras, you just need to pass the same instance of layer to both of the models.
Example Codes:
QUESTION
I have a list [A,B,C,D,E] and a list of indexes [3,2,0,4,1] but the indexes actually points to itself, giving the order to follow.
So starting at 0, next index is 3, then at index 3, the next index is 4,1,2,0 etc.
I can achieve this by looping and updating the index, but my list may have thousands of points, Is there a way to avoid loops and vectorize this?
my code:
...ANSWER
Answered 2021-Jun-15 at 12:46What you're trying to do looks to me like a depth first search in the graph where each node is a number from 0 to n-1 (n = 5 in your example) with a single outgoing edge to the next index it points to. The python solution is already pretty efficient, but if you want something pre-made I think scipy has the solution:
QUESTION
I have a concave hull (not convex) that I have the points for eg: A,B,C,D,E. I've gotten the pairs of points that make up the outer edges. [A,B],[A,E],[C,D],[B,C],[E,D]. (This is a very simplified version)
I want to get the connected points in order (CW or CCW doesn't matter) so I can use them as a contour.
But the pairs are not ordered, you can see A goes to B, then A goes to E, etc. The only solution I had was searching for each point and its next pair sequentially in a loop
Is there a way to solve this using numpy only in a vectorized manner so that its fast for a large array of edges? I know shapely exists but I have trouble installing it and I'd prefer no external dependancies
this is my code:
...ANSWER
Answered 2021-Jun-15 at 08:27You can do this efficiently with a dictionary:
QUESTION
When running the first "almost MWE" code immediately below, which uses conditional panels and a "renderUI" function in the server section, it only runs correctly when I comment out the 3rd line from the bottom, observeEvent(vector.final(periods(),yield_input()),{yield_vector.R <<- unique(vector.final(periods(),yield_input()))}). If I run the code with this line activated, it crashes and I get the error message Error in [: subscript out of bounds which per my research means it is trying to access an array out of its boundary.
ANSWER
Answered 2021-Jun-14 at 22:51Replace the line you commented out with this
QUESTION
I'm creating an int (32 bit) vector with 1024 * 1024 * 1024 elements like so:
...ANSWER
Answered 2021-Jun-14 at 17:01Here are some techniques.
Loop UnrollingQUESTION
I'm trying to train some ML algorithms on some data that I collected, but I received an error for input variables with inconsistent numbers of samples. I'm not really sure what variables needs to be changed or not. I've posted my code below to give you a better understanding of what I'm trying to accomplish:
...ANSWER
Answered 2021-Jun-12 at 12:14The file has to be opened in binary mode.
open(DATA_FILE, 'rb')
QUESTION
I am trying to vectorize a code snippet in pandas:
I have a pandas dataframe generated like this:
ids ftest vals 0 Q52EG 0 0 1 Q52EG 0 1 2 Q52EG 1 2 3 Q52EG 1 3 4 Q52EG 1 4 5 QQ8Q4 0 5 6 QQ8Q4 0 6 7 QQ8Q4 1 7 8 QQ8Q4 1 8 9 QVIPW 1 9If any id in ids column has a value 1 in the ftest column, then all the subsequent rows with same id should be marked as 1 in has_hist column and it doesnt depend on the current ftest value as shown in the dataframe below:
I am doing this using a iterative approach like this:
...ANSWER
Answered 2021-Jun-11 at 13:00Two key functions for this kind of tasks are shift and ffill, applied per group. For this specific question:
QUESTION
I usually hear the term vectorized functions in one of two ways:
- In a very high-level language when the data is passed all-at-once (or at least, in bulk chunks) to a lower-level library that does the calculations in faster way. An example of this would be python's use of
numpyfor array/LA-related stuff. - At the lowest level, when using a specific machine instruction or procedure that makes heavy use of them (such as YMM, ZMM, XMM register instructions).
However, it seems like the term is passed around quite generally, and I wanted to know if there's a third (or even more) ways in which it's used. And this would just be, for example, passing multiple values to a function rather than one (usually done via an array) for example:
...ANSWER
Answered 2021-Jun-10 at 20:43Vectorized code, in the context you seem to be referring to, normally means "an implementation that happens to make use of Single Instruction Multiple Data (SIMD) hardware instructions".
This can sometimes mean that someone manually wrote a version of a function that is equivalent to the canonical one, but happens to make use of SIMD. More often than not, it's something that the compiler does under the hood as part of its optimization passes.
In a very high-level language when the data is passed all-at-once (or at least, in bulk chunks) to a lower-level library that does the calculations in faster way. An example of this would be python's use of numpy for array/LA-related stuff.
That's simply not correct. The process of handing off a big chunk of data to some block of code that goes through it quickly is not vectorization in of itself.
You could say "Now that my code uses numpy, it's vectorized" and be sort of correct, but only transitively. A better way to put it would be "Now that my code uses numpy, it runs a lot faster because numpy is vectorized under the hood.". Importantly though, not all fast libraries to which big chunks of data are passed at once are vectorized.
...Code examples...
Since there is no SIMD instruction in sight in either example, then neither are vectorized yet. It might be true that the second version is more likely to lead to a vectorized program. If that's the case, then we'd say that the program is more vectorizable than the first. However, the program is not vectorized until the compiler makes it so.
QUESTION
What is the most efficient way to remove items from a list based on a function in python (and any common library)?
For example, if I have the following function:
...ANSWER
Answered 2021-Jun-10 at 18:01This is the use case for the builtin filter function:
QUESTION
Surprisingly, after a fair bit of research, I did not find any post sparking a good idea to solve this simple problem.
I have a 1D numpy array of shape (n, ) which is mostly zeros with a few other positive values.
ANSWER
Answered 2021-Jun-10 at 14:12Try using np.diff() like so:
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