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1) It appears to be nearly working on you exemple 3. Not reaching exactly 0 or 1 happens because of the sigmoid function, that can only reach 0 and 1 asymptotically. For binary prediction, it is quite usual to put a cut-off on the output, here 0.5 will do : X<0.5 => 0 and X>=0.5 => 1. 2) As far as I remember this is in line with the theory : you don't ...


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I just faced the same situation. If you need to explicitly build the inverse, check this paper: https://pdfs.semanticscholar.org/f278/b548b5121fd0d09c2e589439b97fad16ece3.pdf In particular, given a Matrix M that you need to invert, you can just do: A = tf.math.real(M) C = tf.math.imag(M) r0 = tf.linalg.pinv(A) @ C y11 = tf.linalg.pinv(C @...


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This is a pure programming question and should be asked at stackoverflow next time. Questions here should have data science at the core of the problem. But you should be able to solve it like this: import numpy as np import pandas as pd df1 = pd.DataFrame({'Object':['cup', 'brick', 'board', 'stone'], 'id':[2, 8, 9, 6]}) df2 = pd.DataFrame({'Thing':['cup', ...


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Scikit uses numpy for pseudo-random number generation. So to fix random state in various scikit calls, you use numpy.random.seed(12345) and then use scikit. You would want to record the random seed when you log the model so you could reproduce the same run later. If your code (or something you call) also uses Python's random number generator, you would set ...


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Your arrays have different shapes on the 0 axis, so you cannot use numpy.stack directly. You can either use padding or put all arrays in a list. Using padding: import numpy as np a0 = np.empty((2,150)) a1 = np.empty((5,150)) a2 = np.empty((10,150)) max_shape = [0,0] for a in [a0, a1, a2]: if max_shape[0] < a.shape[0]: max_shape[0] = a....


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The numpy.reshape() allows you to do reshaping in multiple ways. It usually unravels the array row by row and then reshapes to the way you want it. If you want it to unravel the array in column order you need to use the argument order='F' Let's say the array is a. For the case above, you have a (4, 2, 2) ndarray numpy.reshape(a, (8, 2)) will work. In ...


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I'm not sure it is the best way to do it in r, but you can create a vector simulated temperature for each days of the year by using your reference vector with few temperature by doing the following: 1) You set a dataframe containing few temperatures as references for each month (here, I used lubridate package to manipulate dates): library(lubridate) Date = ...


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What about creating a Pandas DataFrame and adding a new column such as "Temp_simulated" and simulate the temperature?


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