pso | 使用粒子群优化算法来解决01背包问题的可视化代码 | Data Visualization library

I found the solution using *args https://pythonhosted.org/pyswarm/

You need to select the appropriate variables for interaction. I have used pickerInput to select a maximum of 5 variables for interaction. If less than 2 variables are selected, a message is printed. Perhaps there is a more elegant way of doing this. For now, I have provided a quick answer. Please try this

Basically you can use union all statement as approach:

Since you use C++ and not C, I suggest you use string instead of char[] as it may be easier to handle sentences and do operations with them this way, particularly if you come from a higher-level language, or if you are new altogether.

If you wanna print the first letter of each word in the string, you can iterate through the string like this:

There is no need to lock variables. When you are doing parallel calculations you can always think in the terms of Split-Combine strategy (sorry just invent this term in analogy with DataFrames calculation, you may also call it Map-Reduce approach). The idea is that you split calculations around different threads and do them independently and on the final stage you combine results together on a single thread. So your code can look like this (there can be syntax errors, since I can't run the code without Particle and other definitions, but I hope it is enough to give the idea)

Chuck Walbourn Helped me find the solution. The blob did not get the results from compiling similar to D3DCompileFromFile(). By utilizing the IDxcOperationResult in the last parameter of the compiler. You are fetching the results from the compiler like this:

Try with this query:

The 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:

you can find a complete solution below !

The fundamental differences with your approach are the following :

1. Since the Random Forest model takes as input the season feature, optimal discounts must be computed for every season.
2. Inspecting the documentation of pyswarm, the con function yields an output that must comply with con(x) >= 0.0. The correct constraint is therefore 20 - sum(...) and not the other way around. In addition, the units and mrp variable were not given ; I just assumed a value of 1, you might want to change those values.

Additional modifications to your original code include :

1. Preprocessing and pipeline wrappers of sklearn in order to simplify the preprocessing steps.
2. Optimal parameters are stored in an output .xlsx file.
3. The maxiter parameter of the PSO has been set to 5 to speed-up debugging, you might want to set its value to another one (default = 100).

The code is therefore :