From the https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.linprog.html:
Note that by default lb = 0 and ub = None.
So x0 = np.array([ 229.1748166 , -507.05266751, 512.14005547])...
This is probably highly inefficient, but it works for an experimental project. The output contains an extra '0' for the dimensions, as this is the inner dimension. Either drop the first element of thi...
You can use DataFrame.with_row_index():
import polars as pl
df = pl.DataFrame({"test": np.arange(1, 11)})
print(
df.with_row_index()
.with_columns(
pl.when(pl.col("index") < 5)...
Since b contains all the pairs you can efficiently reshape it to square form, indexed by its row/col number, then form the pairs of indices with sliding_window_view and index the square intermediate:...
According to this NumPy issue, this is expected behaviour.
To quote the response to the filed issue:
This is working as documented. Quoting from https://numpy.org/doc/1.21/reference/arrays.indexing.h...
The parameter where = mask without the parameter out is somewhat dangerous. Without a target for the output, the function builds an np.empty array of the appropriate shape, and then replaces some sub...
I think you found out a very unique/interesting and clever solution. Consider also just iterating over columns:
df.select(column / scalars[column.name] for column in df.iter_columns())
or
df.select(p...
The reason you get the millage from this calculation is that the coefficient for millage is 1. While the other coefficients are really small.
Also note you have enought coefficients since you add the...
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