I am trying to sample with replacement a base 2D numpy array with shape of (4,2) by rows, say 10 times. The final output should be a 3D numpy array. Have tried the code below, it works. But is there a way to do it without the for loop? Answer Here’s one vectorized approach – Basic idea is that we
Tag: numpy
How to define a 2D array of (x,y) coordinates
I have a 2d space of (x,y) coordinates that I want to model in python and want to know a way to define the 2d space in python where I can assign multiple values to a point (x,y). Later values at coordinates will be changed based on some coordinate dependent calculations. I thought about using numpy array to create the
replace masked with nan in numpy masked_array
I’d like to replace the masked result with nan. Is there a way to do that directly with numpy’s masked_array? Answer filled method replaces the masked values with the fill value: Or as you do, specify the fill value when defining the array: The masked mean method skips over the filled values:
Generating constraints for optimization in scipy using loop
There are lots of constraints which I will use in optimization using scipy; so I need to generate the constraints by loop. Below there’s a sample of my constraints: There constraints are more than three… I use following loop to generate but I couldn’t get the same output. Answer You are updating cons everytime. Try this And this is more
How to Divide an array in to segments and then do sub segments of the segments using python numpy?
I want to do divide an 8*8 array in to 4 segments(each segment of 4*4 array) as shown below in step2. Then again divide each segment in to other small 4 subsegemnts(each subsegment of 2*2 array) and then find the mean of each subsegment and then find the stabbndard deviation of each segment using the 4 means of the 4
Fastest way to store a numpy array in redis
I’m using redis on an AI project. The idea is to have multiple environment simulators running policies on a lot of cpu cores. The simulators write experience (a list of state/action/reward tuples) to a redis server (replay buffer). Then a training process reads the experience as a dataset to generate a new policy. New policy is deployed to the simulators,
Convert a variable sized numpy array to Tensorflow Tensors
I am trying Tensorflow 2.0 alpha preview and was testing the Eager execution . My doubt is that if you have a numpy array of variable size in middle like and so on for the rest of the array , how does one eagerly convert them to tensors. when I try or I get ValueError: Failed to convert numpy ndarray
using python to calculate Vector Projection
Is there an easier command to compute vector projection? I am instead using the following: Answer Maybe this is what you really want: This should give the projection of vector x onto vector y – see https://en.wikipedia.org/wiki/Vector_projection. Alternatively, if you want to compute the projection of y onto x, then replace y with x in the denominator (norm) of the
How is the dtype of a numpy array calculated internally?
I was just messing around with numpy arrays when I realized the lesser known behavior of the dtypes parameter. It seems to change as the input changes. For example, gives dtype(‘int32’) However, gives dtype(‘int64’) So, my first question is: How is this calculated? Does it make the datatype suitable for the maximum element as a datatype for all the elements?
Finding non-intersection of two pytorch tensors
Thanks everyone in advance for your help! What I’m trying to do in PyTorch is something like numpy’s setdiff1d. For example given the below two tensors: The expected output should be (sorted or unsorted): Ideally the operations are done on GPU and no back and forth between GPU and CPU. Much appreciated! Answer if you don’t want to leave cuda,