I think you’re on the right track. Let’s try this: import scipy import scipy.cluster.hierarchy as sch X = scipy.randn(100, 2) # 100 2-dimensional observations d = sch.distance.pdist(X) # vector of (100 choose 2) pairwise distances L = sch.linkage(d, method=’complete’) ind = sch.fcluster(L, 0.5*d.max(), ‘distance’) ind will give you cluster indices for each of the 100 … Read more
Sounds to me, like you just need to use vectorizer.transform for the test dataset, since the training dataset fixes the vocabulary (you cannot know the full vocabulary including the training set afterall). Just to be clear, thats vectorizer.transform instead of vectorizer.fit_transform.
There are several things going on every time you make a call to scipy.interpolate.griddata: First, a call to sp.spatial.qhull.Delaunay is made to triangulate the irregular grid coordinates. Then, for each point in the new grid, the triangulation is searched to find in which triangle (actually, in which simplex, which in your 3D case will be … Read more
After some trial and error I found out how to interprete the results of scipy.signal.deconvolve() and I post my findings as an answer. Let’s start with a working example code import numpy as np import scipy.signal import matplotlib.pyplot as plt # let the signal be box-like signal = np.repeat([0., 1., 0.], 100) # and use … Read more
The short answer is that G is maintained by the optimizer as part of the minimization process, while the (D_neg, D, and C) arguments are passed in as-is from the args tuple. By default, scipy.optimize.minimize takes a function fun(x) that accepts one argument x (which might be an array or the like) and returns a … Read more
df.values is a numpy array, and accessing values that way is always faster than np.array. scipy.sparse.csr_matrix(df.values) You might need to take the transpose first, like df.values.T. In DataFrames, the columns are axis 0.
Happy to suggest a (possibly obvious) implementation of this, which could be made in pure Python or C/Cython if you’ve got time and space for new dependencies, and need it to be faster. A sparse matrix in N dimensions can assume most elements are empty, so we use a dictionary keyed on tuples: class NDSparseMatrix: … Read more
The solution here is to write a wrapper function that takes your argument list and translates it to variables that the fit function understands. This is really only necessary since I am working qwith someone else’s code, in a more direct application this would work without the wrapper layer. Basically def wrapper_fit_func(x, N, *args): a, … Read more
The volume_overlay3 did not work for me. So I tried your idea to add a bar plot to the candlestick plot. After creating a twin axis for the volume re-position this axis (make it short) and modify the range of the candlestick y-data to avoid collisions. import numpy as np import matplotlib import matplotlib.pyplot as … Read more
Here’s a way to do Bezier curves with numpy: import numpy as np from scipy.special import comb def bernstein_poly(i, n, t): “”” The Bernstein polynomial of n, i as a function of t “”” return comb(n, i) * ( t**(n-i) ) * (1 – t)**i def bezier_curve(points, nTimes=1000): “”” Given a set of control points, … Read more