Change: to: This: gives you list with one element. But this: computes the divisions before the multiplication with alpha. You can multiply a list by an integer: but not by a float: since you cannot have partial elements in a list. Parenthesis can be used for grouping in the mathematical calculations:
If X is your dataframe, try using the .astype method to convert to float when running the model:
You can multiply numpy arrays by scalars and it just works. This is also a very fast and efficient operation. With your example:
Those are not actual errors, but warnings. They are there because you are trying to divide something by zero. Namely, you are setting M[0] = 0 and then dividing by M[0] (at first iteration, where m = 0) and same for P[0]. The question is, what do you want the first values to be? Maybe a solution would be initialize the … Read more
In case you still haven’t found an answer, or in case someone in the future has this question. To solve Ax=b: numpy.linalg.solve uses LAPACK gesv. As mentioned in the documentation of LAPACK, gesv requires A to be square: LA_GESV computes the solution to a real or complex linear system of equations AX = B, where A is a square … Read more
You try to index outside the range: Change to: And it works. You create t with length of M: So you can only access M elements in t. Python always starts indexing with zero. Therefore: will do M loops, while: will do M+1 loops.
I think that your problem is that you are expecting np.append to add the column in-place, but what it does, because of how numpy data is stored, is create a copy of the joined arrays so you need to save the output all_data = np.append(…): Alternative ways: I believe that the only difference between these three functions (as … Read more
If you’re using numpy for your arrays, you can also use Equivalently [Thanks to chbrown for the added shorthand] Explanation The inner function, numpy.isnan returns a boolean/logical array which has the value True everywhere that x is not-a-number. As we want the opposite, we use the logical-not operator, ~ to get an array with Trues … Read more
As you discovered, np.array tries to create a 2d array when given something like You have apply some tricks to get around this default behavior. One is to make the sublists variable in length. It can’t make a 2d array from these, so it resorts to the object array: And you can then append values … Read more
numpy allows the creation of arrays of all ones or all zeros very easily: e.g. numpy.ones((2, 2)) or numpy.zeros((2, 2)) Since True and False are represented in Python as 1 and 0, respectively, we have only to specify this array should be boolean using the optional dtype parameter and we are done. numpy.ones((2, 2), dtype=bool) … Read more