pyswarm | Particle swarm optimization that supports constraints
kandi X-RAY | pyswarm Summary
kandi X-RAY | pyswarm Summary
Particle swarm optimization (PSO) that supports constraints
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pyswarm Key Features
pyswarm Examples and Code Snippets
Lines of Code : 4License : Strong Copyleft (CC BY-SA 4.0)
Copyn_components_RBM, n_components_nn_ = X
n_components_RBM = int(n_components_RBM)
n_components_nn_ = int(n_components_nn_)
import pandas as pd
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.compose import ColumnTransformer
from sklearn.ensemble import RandomForestRegressor
from sklearn.base # myproj/__init__.py
class myclass1:
def __init__(self):
self._thing1 = 1
def doit(self):
print('Hello from myclass1.')
class myclass2:
def __init__(self):
self._thing2 = 2
def doit(self):
ptrain_images = np.reshape(train_images, (-1,1,512,512))
train_images = np.array([imgMatricesNP[0:79]])
test_images = np.reshape(test_images, (-1,1,512,512))
test_imdef f_ode(t,u): return [ u[1], -u[0] ]
tspan = np.linspace(0,1,51);
u_init = [1.0, 0.0]
u = odeint(f_ode, u_init, tspan, tfirst=True)
res = solve_ivp(f_ode, tspan[[0,-1]], u_init, t_eval=tswhich pip
which pip3 # If it belongs to your myenv, then do the next line
pip3 install pyswarm
Community Discussions
Trending Discussions on pyswarm
QUESTION
I implemented BPSO as a feature selection approach using the pyswarms library. I followed this tutorial.
Is there a way to limit the maximum number of features? If not, are there other particle swarm (or genetic/simulated annealing) python-implementations that have this functionality?
...ANSWER
Answered 2021-Mar-16 at 11:36An easy way is to introduce a penalty for using any number of features. The in the following code a objective i defined
QUESTION
I wonder how can I pass a specific variable to a function using Pyswarm in Python.
Check the example bellow
...ANSWER
Answered 2021-Mar-08 at 22:31I found the solution using *args https://pythonhosted.org/pyswarm/
QUESTION
I am training and testing a dataset using deep belief concept. Facing an numpy.float64 error while working with it:
ANSWER
Answered 2020-Oct-29 at 07:07The n_components_RBM (and maybe n_components_nn_) is probably not an integer but a floating point. You can convert it to integer before passing it to BernoulliRBM using something like:
QUESTION
I need to find optimal discount for each product (in e.g. A, B, C) so that I can maximize total sales. I have existing Random Forest models for each product that map discount and season to sales. How do I combine these models and feed them to an optimiser to find the optimum discount per product?
Reason for model selection:
- RF: it's able to give better(w.r.t linear models) relation between predictors and response(sales_uplift_norm).
- PSO: suggested in many white papers(available at researchgate/IEEE), also availability of the package in python here and here.
Input data: sample data used to build model at product level. Glance of the data as below:
Idea/Steps followed by me:
- Build RF model per products
ANSWER
Answered 2020-Aug-23 at 14:32you can find a complete solution below !
The fundamental differences with your approach are the following :
- Since the Random Forest model takes as input the
seasonfeature, optimal discounts must be computed for every season. - Inspecting the documentation of pyswarm, the
confunction yields an output that must comply withcon(x) >= 0.0. The correct constraint is therefore20 - sum(...)and not the other way around. In addition, theunitsandmrpvariable were not given ; I just assumed a value of 1, you might want to change those values.
Additional modifications to your original code include :
- Preprocessing and pipeline wrappers of
sklearnin order to simplify the preprocessing steps. - Optimal parameters are stored in an output
.xlsxfile. - The
maxiterparameter of the PSO has been set to5to speed-up debugging, you might want to set its value to another one (default =100).
The code is therefore :
QUESTION
I created a package in Pypi and did the following setting. Lets say that my package is called "myproj". So I put all the different files in myproj/ And to use them I have to do
from myproj.myclass1 import myclass1
And I would like it to work like
...ANSWER
Answered 2020-May-08 at 14:12Put your classes (eg, myclass1 and myclass2) into the file myproj/__init__.py.
QUESTION
I am a beginner at working with CNNs.
So, I am building a 2D convolutional neural network that predicts brain tumor type and have a question about NumPy arrays. The input-shape of my model is (1, 512, 512) as (channels, img_height, img_width). The 4th dimension is num_images which seems to be automatically defined by TensorFlow. This is just a quick background. I have 3064 ".mat" extension files with MRI scans of brain tumors. Everything is setup. I converted ".mat" files into numpy matrices and appended the entire list of matrices in a single numpy array to pass as input for the CNN. I also have the corresponding labels (index-linked to the images when passing input into the model) as a numpy array. All the numbers are of float type in both images and labels.
Again, my input shape is (1, 512, 512). However, when fitting my model I get the following error:
ValueError: Error when checking input: expected conv2d_130_input to have shape (1, 512, 512) but got array with shape (79, 512, 512)
So, I am slicing my NumPy arrays to create train_images, train_labels, test_images, test_labels. I have verified the length of each both train and test sets with there labels match. They are also arrays, I checked multiple times. And this is a value error. So, how do I fix this?
I don't even know where the input shape became (79,512,512). I have a loop to convert f"{n}.mat" images to a matrix. I am using 100 images to test and have 80 train and 20 test. I think the mistake is here, the input shape is (channels, img-hght, img-wdth), but the number of images left to train is being placed in the channel's value instead. So, the input is being placed as (num_images, img-hght, img-wdth). This is wrong and should be changed, but I don't know how to do it. Or, I could be wrong and what I said might not make sense. I am providing all the code, running it on Colab. Make sure to change the image paths if you download the code and want to run it in order to help me out. Thanks a lot!
Dataset: https://figshare.com/articles/brain_tumor_dataset/1512427/5
...ANSWER
Answered 2020-Feb-09 at 06:48Add the line:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install pyswarm
You can use pyswarm like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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