from sklearn.utils import shuffle import numpy as np X = np.array([ [0, 0, 0], [1, 1, 1], [2, 2, 2], [3, 3, 3], [4, 4, 4] ]) y = np.array([0, 1, 2, 3, 4]) X, y = shuffle(X, y) print(X) print(y) Output: [[1 1 1] [3 3 3] [0 0 0] [2 2 2] [4 … Read more
The numpy function bincount was made exactly for this purpose and I’m sure it will be much faster than the other methods for all sizes of inputs: data = [1,2,3,4,5,6] ids = [0,0,1,2,2,1] np.bincount(ids, weights=data) #returns [3,9,9] as a float64 array The i-th element of the output is the sum of all the data elements … Read more
Chunk random_days_panel instead of historical_data and use da.map_blocks: def dask_way(sim_count, sim_days, hist_days): # shared historical data # on a cluster you’d load this on each worker, e.g. from a NPZ file historical_data = np.random.normal(111.51, 10, size=hist_days) random_days_panel = da.random.randint( 1, hist_days, size=(1, 1, sim_count, sim_days) ) future_panel = da.map_blocks( lambda chunk: historical_data[chunk], random_days_panel, dtype=float ) … Read more
Try to approach the problem from the Python side: Python is a great glue language, I would suggest you to have Python run your Matlab and C programs. Python has: Numpy PyLab Matplotlib IPython Thus, the combination is a good alternative for almost any existing Matlab module.
You could use a function like this: def nan_ints(df, convert_strings=False, subset=None): types = [“int64”, “float64”] if subset is None: subset = list(df) if convert_strings: types.append(“object”) for col in subset: if df[col].dtype in types: df[col] = ( df[col].astype(float, errors=”ignore”).astype(“Int64″, errors=”ignore”) ) return df It iterates through each column and coverts it to an Int64 if it … Read more
After some trial and error I found out how to interprete the results of scipy.signal.deconvolve() and I post my findings as an answer. Let’s start with a working example code import numpy as np import scipy.signal import matplotlib.pyplot as plt # let the signal be box-like signal = np.repeat([0., 1., 0.], 100) # and use … Read more
Use np.nextafter. >>> import numpy as np >>> np.nextafter(0, 1) 4.9406564584124654e-324 >>> np.nextafter(np.float32(0), np.float32(1)) 1.4012985e-45
Modifying the sample axis, e.g. removing samples, does not (yet?) comply with the scikit-learn transformer API. So if you need to do this, you should do it outside any calls to scikit learn, as preprocessing. As it is now, the transformer API is used to transform the features of a given sample into something new. … Read more
I think enums are safer especially for larger systems with multiple developers. As soon as the need arises to change the value of such an enum, looking up and replacing a string in many places is not my idea of fun 🙂 The most important criteria IMHO is the usage: for use in a module … Read more
To get a list of evenly spaced indices, use np.linspace: idx = np.round(np.linspace(0, len(arr) – 1, numElems)).astype(int) Next, index back into arr to get the corresponding values: arr[idx] Always use rounding before casting to integers. Internally, linspace calls astype when the dtype argument is provided. Therefore, this method is NOT equivalent to: # this simply … Read more