Do you care about the actual precision of the result, or about getting the exact same digits back from your two calculations? If you just want the same digits, you could use np.around() to round the results to some appropriate number of decimal places. However, by doing this you’ll only reduce the precision of the … Read more
Here’s what I put together. I tried to closely emulate your screenshot. Given import numpy as np import scipy as sp import scipy.stats as stats import matplotlib.pyplot as plt %matplotlib inline # Raw Data heights = np.array([50,52,53,54,58,60,62,64,66,67,68,70,72,74,76,55,50,45,65]) weights = np.array([25,50,55,75,80,85,50,65,85,55,45,45,50,75,95,65,50,40,45]) Two detailed options to plot confidence intervals: def plot_ci_manual(t, s_err, n, x, x2, y2, ax=None): … Read more
Sympy makes this straightforward. >>> from sympy import Matrix >>> A = [[2, 3, 5], [-4, 2, 3], [0, 0, 0]] >>> A = Matrix(A) >>> A * A.nullspace()[0] Matrix([ [0], [0], [0]]) >>> A.nullspace() [Matrix([ [-1/16], [-13/8], [ 1]])]
In the solution below I used python3.4 as binary, but it’s safe to use with any version or binary of python. it works fine on windows too (except the downloading “get-pip.py” with wget obviously but just save the file locally and run it with python, see below). This is great if you have multiple versions … Read more
The message “intermediate result is being cached” is just a blind guess in the canned message reported by %timeit. It may or may not be true, and you should not assume it is correct. In particular, one of the most common reasons for the first run being slowest is that the array is in the … Read more
For short arrays, using sets is probably the clearest and most readable way to do it. Another way is to use numpy.intersect1d. You’ll have to trick it into treating the rows as a single value, though… This makes things a bit less readable… import numpy as np A = np.array([[1,4],[2,5],[3,6]]) B = np.array([[1,4],[3,6],[7,8]]) nrows, ncols … Read more
Change index = output.data.numpy().argmax() to index = output.cpu().data.numpy().argmax() This means data is first moved to cpu and then converted to numpy array.
I think the most crucial point to understand here is the difference between a torch.tensor and np.ndarray: While both objects are used to store n-dimensional matrices (aka “Tensors”), torch.tensors has an additional “layer” – which is storing the computational graph leading to the associated n-dimensional matrix. So, if you are only interested in efficient and … Read more
We can use masked arrays for this: >>> from numpy import * >>> m = array([[1,0], [2,3]]) >>> x = ma.log(m) >>> print x.filled(0) [[ 0. 0. ] [ 0.69314718 1.09861229]]
Straightforward way to do that is: import numpy as np a=np.array([[1,2,3],[3,4,5]]) b=np.array([[1,2,3],[1,2,3]]) np.sum(a*b, axis=1) which avoids the python loop and is faster in cases like: def npsumdot(x, y): return np.sum(x*y, axis=1) def loopdot(x, y): result = np.empty((x.shape[0])) for i in range(x.shape[0]): result[i] = np.dot(x[i], y[i]) return result timeit npsumdot(np.random.rand(500000,50),np.random.rand(500000,50)) # 1 loops, best of 3: … Read more