The main reason to avoid using the matrix class is that a) it’s inherently 2-dimensional, and b) there’s additional overhead compared to a “normal” numpy array. If all you’re doing is linear algebra, then by all means, feel free to use the matrix class… Personally I find it more trouble than it’s worth, though. For … Read more
Either read it in with ‘scan’, or just do as.vector() on the matrix. You might want to transpose the matrix first if you want it by rows or columns. > m=matrix(1:12,3,4) > m [,1] [,2] [,3] [,4] [1,] 1 4 7 10 [2,] 2 5 8 11 [3,] 3 6 9 12 > as.vector(m) [1] … Read more
There are many ways to do this. This answer starts with what is quickly becoming the standard method, but also includes older methods and various other methods from answers to similar questions scattered around this site. tmp <- data.frame(x=gl(2,3, labels=letters[24:25]), y=gl(3,1,6, labels=letters[1:3]), z=c(1,2,3,3,3,2)) Using the tidyverse: The new cool new way to do this is … Read more
For elementwise multiplication of matrix objects, you can use numpy.multiply: import numpy as np a = np.array([[1,2],[3,4]]) b = np.array([[5,6],[7,8]]) np.multiply(a,b) Result array([[ 5, 12], [21, 32]]) However, you should really use array instead of matrix. matrix objects have all sorts of horrible incompatibilities with regular ndarrays. With ndarrays, you can just use * for … Read more
you can use plt.matshow() instead of plt.imshow() or you can use seaborn module’s heatmap (see documentation) to plot the confusion matrix import seaborn as sn import pandas as pd import matplotlib.pyplot as plt array = [[33,2,0,0,0,0,0,0,0,1,3], [3,31,0,0,0,0,0,0,0,0,0], [0,4,41,0,0,0,0,0,0,0,1], [0,1,0,30,0,6,0,0,0,0,1], [0,0,0,0,38,10,0,0,0,0,0], [0,0,0,3,1,39,0,0,0,0,4], [0,2,2,0,4,1,31,0,0,0,2], [0,1,0,0,0,0,0,36,0,2,0], [0,0,0,0,0,0,1,5,37,5,1], [3,0,0,0,0,0,0,0,0,39,0], [0,0,0,0,0,0,0,0,0,0,38]] df_cm = pd.DataFrame(array, index = [i for i in … Read more
You simply use the apply() function: R> M <- matrix(1:6, nrow=3, byrow=TRUE) R> M [,1] [,2] [1,] 1 2 [2,] 3 4 [3,] 5 6 R> apply(M, 1, function(x) 2*x[1]+x[2]) [1] 4 10 16 R> This takes a matrix and applies a (silly) function to each row. You pass extra arguments to the function as … Read more
I’m the author of Java Matrix Benchmark (JMatBench) and I’ll give my thoughts on this discussion. There are significant difference between Java libraries and while there is no clear winner across the whole range of operations, there are a few clear leaders as can be seen in the latest performance results (October 2013). If you … Read more
To get the row numbers where at least one item is larger than 15: >>> np.where(np.any(e>15, axis=1)) (array([1, 2], dtype=int64),)
Part of the answer is contained already in your question: You use data frames if columns (variables) can be expected to be of different types (numeric/character/logical etc.). Matrices are for data of the same type. Consequently, the choice matrix/data.frame is only problematic if you have data of the same type. The answer depends on what … Read more
shape is a property of both numpy ndarray’s and matrices. A.shape will return a tuple (m, n), where m is the number of rows, and n is the number of columns. In fact, the numpy matrix object is built on top of the ndarray object, one of numpy’s two fundamental objects (along with a universal … Read more