I would need to optimize a function f with respects to a vector x, that takes as input a constant matrix m and returns a scalar v >= 0. MWE with random numbers: How do I set up the constraint for the output value? As far as I read in the doc I can only set constraints for the inputs.

# Tag: scipy

## Solve a linear system of equations with bounds using LSQR/LSMR SciPy

Question is quite straight forward. I have an overdetermined system I am attempting to use SciPy LSQR (or LSMR) to solve. However, I cannot find anywhere in the docs on how to set restraints for the minimization. E.G. Let’s say this is the output: We can see the solution arrive to -0.1, which is great. However, I’d like to set

## Check result of chi square test on pandas columns data

I wrote the test according to an approach I found. When looking in Stack Overflow I saw another approach (can be seen here) which was a little more complicated, and made me wonder if I chose the right one. I’m looking for ways to check if my calculation is correct. Here is the relevant code: Any suggestions will be welcomed.

## SciPy Minimize doesn’t pass all guesses on?

I am trying to minimize a function of two variables using SciPy. The function itself is a chain of multiple lambda functions (makes it complicated but unfortunately it is the easiest way to write the expressions I need). However, when using SciPy’s minimize routine, I get the error “TypeError: () missing 1 required positional argument: ‘labour'” Strangely enough, if I

## How to make dotted line in a binary array

how to create the dotted line in the below NumPy array bh=make_figure(b,’gh’) requirement: how to convert element 1 into 0 with the step of two expected outputs is like I tried with a brute force algorithm, but I am not able to find the solution output array looks like for visual representation like making a dotted line Answer Here’s one

## Optimization problem for S-I-S model using python

I have a susceptible-infectious-susceptible model, to which I’ve written the following python code, And I’m solving it using the following code, This part is fine. I’m having trouble finding the double derivative and optimizing it for the value of the beta parameter. The problem is that the beta is not given and since that parameter is within the exponential function,

## fit multiple parametric curves with scipy python

I am trying to fit two curve into one equation. y = (a * exp(b * (T^-1)))cexp(d100)(x^0.5) for y1, T =10, for y2, T =25. how do a get a,b,c,d I have a code that simplified to fit one data. I don’t know how to do both. I find a similar question with solution but I can’t follow.. fit multiple

## Iterating row-wise over 2 pandas dataframes and passing these vectors as args to function

I’d like to iterate row-wise over 2 identically-shaped dataframes, passing the rows from each as vectors to a function without using loops. Essentially something similar to R’s mapply. I’ve investigated a little and the best that I’ve seen uses map in a list comprehension, but I’m not doing it correctly. Even if we get this to work, though, it seems

## scipy.stats.cumfreq() isn’t the cumulative frequency I’m looking for

Reading a statistic book, I’m also training with Python. My book asks me to calculate the cumulative workforce and the cumulative frequency of a simple list of jobs. Secteur Nombre d’emplois Agriculture 21143585 Construction 35197834 Industrie 69941779 Fabrication 64298386 Services 368931820 I wrote this Python program: that responds me: And if my book agrees for the cumulative workforce, it doesn’t

## How to fill in missing center of a 2d gaussian

I have a 2d gaussian whose center has been destroyed by pixel saturation. I need the center to be filled in because a poorly filled in center will confuse a neural network I’m trying to train. See below: The scattered nan values I can handle fairly easily, but the large cluster in the gaussian’s center I cannot. I’ve tried various