numpy convention for storing time series of vectors and matrices – items in rows or columns?
I’m working with discrete-time simulations of ODEs with time varying parameters. I have time series of various data (e.g. time series of state vectors generated by solve_ivp, time series of system matrices generated by my control algorithm, time series of system matrices in modal form, and so on).
numpy convention for storing time series of vectors and matrices – items in rows or columns?
I’m working with discrete-time simulations of ODEs with time varying parameters. I have time series of various data (e.g. time series of state vectors generated by solve_ivp, time series of system matrices generated by my control algorithm, time series of system matrices in modal form, and so on).
numpy convention for storing time series of vectors and matrices – items in rows or columns?
I’m working with discrete-time simulations of ODEs with time varying parameters. I have time series of various data (e.g. time series of state vectors generated by solve_ivp, time series of system matrices generated by my control algorithm, time series of system matrices in modal form, and so on).
Add two numpy arrays with an offset
Lets say I have two arrays:
numpy 2d arrays, remove duplicate rows
I have 2 2d numpy arrays, Array A and Array B.
Linear Regression Using Normal Equation using @ symbol
Write a Python function that performs linear regression using the normal equation. The function should take a matrix X (features) and a vector y (target) as input, and return the coefficients of the linear regression model. Round your answer to four decimal places, -0.0 is a valid result for rounding a very small number.
Use numpy to mask a column containing only zeros (3D array)
I need to achieve basically the solution in this post but for a higher dimensional array. I have an array of shape (100, 24, 29) with dims corresponding to (timepoint x W x H), and I want to delete any columns (from the third dimension) that contain only zeros. I have tried to adapt the answer from the linked post above in this toy example:
Use numpy to mask a row containing only zeros (3D array)
I need to achieve basically the solution in this post but for a higher dimensional array. I have an array of shape (100, 24, 29) with dims corresponding to (timepoint x W x H), and I want to delete any columns (from the third dimension) that contain only zeros. I have tried to adapt the answer from the linked post above in this toy example:
AttributeError: module ‘numpy’ has no attribute ‘object’ with import demands
I got this error:
AttributeError: module ‘numpy’ has no attribute ‘object’.
np.object was a deprecated alias for the builtin object. To avoid this error in existing code, use object by itself. Doing this will not modify any behavior and is safe.
The aliases was originally deprecated in NumPy 1.20
Numpy fails for unclear reason
I am calling Numpy in my subroutine: