pulp solution After some research, I don’t think your objective function is linear. I recreated the problem in the Python pulp library but pulp doesn’t like that we’re dividing by a float and ‘LpAffineExpression’. This answer suggests that linear programming “doesn’t understand divisions” but that comment is in context of adding constraints, not the objective … Read more
You may try mlxtend which got various selection methods. from mlxtend.feature_selection import SequentialFeatureSelector as sfs clf = LinearRegression() # Build step forward feature selection sfs1 = sfs(clf,k_features = 10,forward=True,floating=False, scoring=’r2′,cv=5) # Perform SFFS sfs1 = sfs1.fit(X_train, y_train)
Scipy doesn’t have a way to do this without copying the data but you can do it yourself by changing the attributes that define the sparse matrix. There are 4 attributes that make up the csr_matrix: data: An array containing the actual values in the matrix indices: An array containing the column index corresponding to … Read more
This has been implemented in scikit-learn sklearn.preprocessing.normalize. from sklearn.preprocessing import normalize w_normalized = normalize(w, norm=’l1′, axis=1) axis=1 should normalize by rows, axis=0 to normalize by column. Use the optional argument copy=False to modify the matrix in place.
Correlation (default ‘valid’ case) between two 2D arrays: You can simply use matrix-multiplication np.dot like so – out = np.dot(arr_one,arr_two.T) Correlation with the default “valid” case between each pairwise row combinations (row1,row2) of the two input arrays would correspond to multiplication result at each (row1,row2) position. Row-wise Correlation Coefficient calculation for two 2D arrays: def … Read more
You just need to unstack your series: >>> s.unstack(level=1) second one two first bar -0.713374 0.556993 baz 0.523611 0.328348 foo 0.338351 -0.571854 qux 0.036694 -0.161852
There is no automatic feature to do such a thing, but you could loop through each point and put text in the appropriate location: import matplotlib.pyplot as plt import numpy as np data = np.random.rand(5, 4) heatmap = plt.pcolor(data) for y in range(data.shape[0]): for x in range(data.shape[1]): plt.text(x + 0.5, y + 0.5, ‘%.4f’ % … Read more
What’s wrong with just separating it out into real and imaginary parts? scipy.integrate.quad requires the integrated function return floats (aka real numbers) for the algorithm it uses. import scipy from scipy.integrate import quad def complex_quadrature(func, a, b, **kwargs): def real_func(x): return scipy.real(func(x)) def imag_func(x): return scipy.imag(func(x)) real_integral = quad(real_func, a, b, **kwargs) imag_integral = quad(imag_func, … Read more
I’m not sure completely understand, but it appears you are trying to re-index each axis of the array based on sorts of the dendrogram indicies. I guess that assumes there is some comparative logic in each branch delineation. If this is the case then would this work(?): >>> x_idxs = [(0,1,0,0),(0,1,1,1),(0,1,1),(0,0,1),(1,1,1,1),(0,0,0,0)] >>> y_idxs = [(1,1),(0,1),(1,0),(0,0)] … Read more
I would probably address this by using dask to load your data in a streaming fashion. For example, you can create a dask dataframe as follows: import dask.dataframe as ddf data = ddf.read_csv(‘test.csv’) This data object hasn’t actually done anything at this point; it just contains a “recipe” of sorts to read the dataframe from … Read more