Category : model-fitting

import numpy as np from scipy.stats import levy # read data from DataSamples/datasample8.csv in numpy array format. data_8 = np.array([float(x.strip()) for x in open(‘DataSamples/datasample8.csv’).readlines()]) # using MLE to find the parameters of the Levy distribution. levy_mu_8, levy_sigma_8 = levy.fit(data_8) I am getting a RuntimeWarning: invalid value encountered in double_scalars when executing the above code to ..

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I want to fit the following model: To some fluorescence measurements ([YFP] over time). Basically I can measure the change of YFP over time, but not the change of x. After navigating through different solutions in overflow (and trying various of the solutions proposed), I finished getting pretty close with Symfit. However, when I try ..

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I am trying to use LSTM model(already trained) in csv data. Before using data I have to normalize and reshape it to get prediction. I am trying to normalize whole data to use in model inference. I tried in this way: model = load_model(‘./anomaly_model.h5’) class Inference: @classmethod def predict_anomaly(self): data_out = {} #read the data ..

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I have the following data generated using: from scipy.stats import skewnorm import matplotlib.pyplot as plt import numpy as np numValues = 10000 maxValue = 100 skewness = -5 data = skewnorm.rvs(a = skewness,loc=maxValue, size=numValues) for guassian model, I have calculated the AIC from below: from sklearn.mixture import GaussianMixture gmm = GaussianMixture(n_components = 1).fit(X=np.expand_dims(data,1)) gmm_aic = ..

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I would like to check the value of hyperparameters of a scikit-learn model before and after fitting: from sklearn.ensemble import RandomForestClassifier from sklearn.datasets import make_classification from sklearn.model_selection import train_test_split X, y = make_classification(n_samples=1000, n_features=4, n_informative=2, n_redundant=0) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3) clf = RandomForestClassifier(random_state=0) print(clf.get_params()) clf.fit(X_train, y_train) print(clf.get_params()) It gives me the ..

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I have a set of data that I have been attempting to fit. An intermediate part of obtaining model predictions involves solving a differential equation over time using a numerical method. I have attempted fitting the data with a least squares method in Matlab. It gets a solution in a reasonable amount of time; however, ..

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