I have two python lists of strings that I would like to compare. The first is my main list, containing a series of long codes. The second is a list of partial strings. The desired result is a mask of list 1, populated by the substrings from list two. If no match is found, list3 can return 0, np.nan, ‘-‘,
Tag: numpy
error: (-215:Assertion failed) scn + 1 == m.cols in function ‘cv::perspectiveTransform’
Below is a python script that calculates the homography between two images and then map a desired point from one image to another However, when i display the image that contains the mapped point it returns the following error: According to my knowledge this error means that parameter assigned to the function perspective transform is not correct or not being
TF.Keras model.predict is slower than straight Numpy?
Thanks, everyone for trying to help me understand the issue below. I have updated the question and produced a CPU-only run and GPU-only of the run. In general, it also appears that in either case a direct numpy calculation hundreds of times faster than the model. predict(). Hopefully, this clarifies that this does not appear to be a CPU vs
ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type numpy.ndarray). in trying to predict tesla stock
In the end you can see that i have tried converting this into a numpy array but I don’t understand why tensorflow dosen’t support it? I have looked at the other related pages but none seemed to help. Is there some other format i have to do to the data in order to properly fit in model? this is what
Not able to install jaxlib
I am trying to install jaxlib on my windows 10 by the following command which I found on the documentation.. pip install jaxlib It shows the following error Answer Jaxlib is not supported on windows you can see it here.. https://github.com/google/jax/issues/438
What does numpy.ix_() function do and what is the output used for?
Below shows the output from numpy.ix_() function. What is the use of the output? It’s structure is quite unique. Answer According to numpy doc: Construct an open mesh from multiple sequences. This function takes N 1-D sequences and returns N outputs with N dimensions each, such that the shape is 1 in all but one dimension and the dimension with
calling list() method over array of one element raises TypeError: iteration over a 0-d array
calling list() method over pandas dataframe single row raises an error. For example, Now, the below is fine but, raises: How to address this issue? Answer You can use pd.Series.tolist() here.
Why is ‘scipy.sparse.linalg.spilu’ less efficient than ‘scipy.linalg.lu’ for sparse matrix?
I posted this question on https://scicomp.stackexchange.com, but received no attention. As long as I get answer in one of them, I will inform in the other. I have a matrix B which is sparse and try to utilize a function scipy.sparse.linalg.spilu specialized for sparse matrix to factorize B. Could you please explain why this function is significantly less efficient than
Running numpy using anaconda and VS Code
I am trying to get VScode to work with anaconda but having issues with numpy. I have managed to get VScode to use the right python environment From VScode (ctrl+shift+P, type Python:Select Interpreter and select the appropriate option). However, when I type “import numpy” in to my script, I get the following error message: I have successfully called the ‘import
Improvement on copy array elements numpy
I have a question regarding variable assignation and memory allocation in a simple case. Imagine that I am initialising a state vector x, with initial value x0. I am then making iterative updates on that state vector with buffer array X and after each iteration, I store the new state vector in a storage list L. An outline of my