I am using Python/NumPy, and I have two arrays like the following: And I would like to create a new array: and append items to it. So for example if the new items to append are: Then now array3 would be an array with two rows and two columns like the one shown below: I … Read more
As fuglede says, the issue here is that np.float64 can’t handle a number as large as exp(1234.1). Try using np.float128 instead: Note however, that there are certain quirks with using extended precision. It may not work on Windows; you don’t actually get the full 128 bits of precision; and you might lose the precision whenever the number passes through pure … Read more
It seems you need DataFrame.var: Normalized by N-1 by default. This can be changed using the ddof argument Sample: If need numpy solutions with numpy.var: Differences are because by default ddof=1 in pandas, but you can change it to 0:
At least one item in your list is either not three dimensional, or its second or third dimension does not match the other elements. If only the first dimension does not match, the arrays are still matched, but as individual objects, no attempt is made to reconcile them into a new (four dimensional) array. Some … Read more
numpy.zeros Return a new array of given shape and type, filled with zeros. or numpy.ones Return a new array of given shape and type, filled with ones. or numpy.empty Return a new array of given shape and type, without initializing entries. However, the mentality in which we construct an array by appending elements to a … Read more
It means: Source. The first character specifies the kind of data and the remaining characters specify the number of bytes per item, except for Unicode, where it is interpreted as the number of characters. The item size must correspond to an existing type, or an error will be raised. The supported kinds are to an … Read more
Use numpy.array to use shape attribute. NOTE X.shape returns 3-items tuple for the given array; [n, T] = X.shape raises ValueError.
Answered by @hpaulj and @ForceBru in the comments. Missing a set of [] brackets.
For elementwise multiplication of matrix objects, you can use numpy.multiply: Result 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 elementwise multiplication: If you’re on Python 3.5+, you don’t even lose the ability to perform matrix multiplication with an operator, because @ does matrix multiplication now:
Use numpy.linalg.norm: You can find the theory behind this in Introduction to Data Mining This works because the Euclidean distance is the l2 norm, and the default value of the ord parameter in numpy.linalg.norm is 2.