random-seed

来源： https://stackoverflow.com/questions/57220804/performance-difference-between-numpy-random-and-random-random-in-python

来源： https://stackoverflow.com/questions/57220804/performance-difference-between-numpy-random-and-random-random-in-python

问题 I am running a monte-carlo simulation in parallel using joblib . I noticed however although my seeds were fixed my results kept changing. However, when I ran the process in series it remained constant as I expect. Below I implement a small example, simulating the mean for a normal distribution with higher variance. Load Libraries and define function import numpy as np from joblib import Parallel, delayed def _estimate_mean(): np.random.seed(0) x = np.random.normal(0, 2, size=100) return np

问题 I am running a monte-carlo simulation in parallel using joblib . I noticed however although my seeds were fixed my results kept changing. However, when I ran the process in series it remained constant as I expect. Below I implement a small example, simulating the mean for a normal distribution with higher variance. Load Libraries and define function import numpy as np from joblib import Parallel, delayed def _estimate_mean(): np.random.seed(0) x = np.random.normal(0, 2, size=100) return np

问题 I am running a monte-carlo simulation in parallel using joblib . I noticed however although my seeds were fixed my results kept changing. However, when I ran the process in series it remained constant as I expect. Below I implement a small example, simulating the mean for a normal distribution with higher variance. Load Libraries and define function import numpy as np from joblib import Parallel, delayed def _estimate_mean(): np.random.seed(0) x = np.random.normal(0, 2, size=100) return np

问题 I'm trying to generate Erdos-Renyi graphs using boost graph library. In the code below, which is taken from The Boost 1.72 documentation the networks always have the same number of edges (they should not, for particular p values). I have tried using different random seeds to no avail. Thanks for any help. #include <boost/graph/adjacency_list.hpp> #include <boost/graph/erdos_renyi_generator.hpp> #include <boost/random/linear_congruential.hpp> #include <iostream> using namespace std; typedef

问题 I'm trying to generate Erdos-Renyi graphs using boost graph library. In the code below, which is taken from The Boost 1.72 documentation the networks always have the same number of edges (they should not, for particular p values). I have tried using different random seeds to no avail. Thanks for any help. #include <boost/graph/adjacency_list.hpp> #include <boost/graph/erdos_renyi_generator.hpp> #include <boost/random/linear_congruential.hpp> #include <iostream> using namespace std; typedef

问题 I'm currently training a convolutional neural network using a conv2D layer defined like this: conv1 = tf.keras.layers.Conv2D(filters=64, kernel_size=(3,3), padding='SAME', activation='relu')(inputs) My understanding is that the default kernel_initializer is glorot_uniform which has a default seed of 'none': tf.keras.layers.Conv2D( filters, kernel_size, strides=(1, 1), padding='valid', data_format=None, dilation_rate=(1, 1), activation=None, use_bias=True, kernel_initializer='glorot_uniform',

问题 Starting from the Tensorflow CNN example, I'm trying to modify the model to have multiple images as an input (so that the input has not just 3 input channels, but multiples of 3 by stacking images). To augment the input, I try to use random image operations, such as flipping, contrast and brightness provided in TensorFlow. My current solution to apply the same random distortion to all input images is to use a fixed seed value for these operations: def distort_image(image): flipped_image = tf

问题 I am trying to write an OpenMP code in which each thread will work on big arrays of uniformly distributed random numbers between 0 and 1. Each thread needs to have different and independent random number distributions. In addition, the random number distributions need to be different every time the code is called. This is what I am using right now. Does this always guarantee each thread has its own/different random number sequences? Will the sequences be different every time the code is