normalization

问题 Please find below my data ( rows are disease group 0= control, 1=Ulcerative Colitis and 2=Crohns), columns are gene expression values. structure(c(5.54312e-05, 5.6112e-06, 9.74312e-05, 1.3612e-06, 1.29312e-05, 7.2512e-06, 0.0002159302, 3.6312e-06, 0.0001467552, 1.53312e-05, 0.0009132182, 1.9312e-06, 0.0074214952, 0.0006480372, 5.1312e-06, 6.1812e-06, 4.7612e-06, 0.0001199302, 0.0008845182, 0.0008506632, 0.0002366382, 7.3912e-06, 8.5112e-06, 2.63312e-05, 0.0013685242, 1.12312e-05, 0.0001775992

问题 Let's say I have a data frame of 1000 rows and 3 columns (column t0, t4 and t8). Each column represents a time point (0 hours, 4 hours and 8 hours). The data is gene expression: numeric (float): row.name t0 t4 t8 ENSG00000000419.8 1780.00 1837.00 1011.00 ENSG00000000457.9 859.00 348.39 179.00 ENSG00000000460.12 1333.00 899.00 508.00 I need to normalize the data according to a known result. I know that the average half-life of all rows (genes) should be 10 hours. So I need to find the

问题 I'm given an image (32, 32, 3) and two vectors (3,) that represent mean and std. I'm trying normalize the image by getting the image into a state where I can subtract the mean and divide by the std but I'm getting the following error when I try to plot it. ValueError: Floating point image RGB values must be in the 0..1 range. I understand the error so I'm thinking I'm not performing the correct operations when I try to normalize. Below is the code I'm trying to use normalize the image. mean

问题 I calculated a confusion matrix for my classifier using the method confusion_matrix() from the sklearn package. The diagonal elements of the confusion matrix represent the number of points for which the predicted label is equal to the true label, while off-diagonal elements are those that are mislabeled by the classifier. I would like to normalize my confusion matrix so that it contains only numbers between 0 and 1. I would like to read the percentage of correctly classified samples from the

问题 Consider the following code: import numpy as np A = np.array([[.8, .6], [.1, 0]]) B1 = tf.keras.utils.normalize(A, axis=0, order=1) B2 = tf.keras.utils.normalize(A, axis=0, order=2) print('A:') print(A) print('B1:') print(B1) print('B2:') print(B2) which returns A: [[0.8 0.6] [0.1 0. ]] B1: [[0.88888889 1. ] [0.11111111 0. ]] B2: [[0.99227788 1. ] [0.12403473 0. ]] I understand how B1 is computed via order=1 such that each entry in A is divided by the sum of the elements in its column. For

问题 Consider the following code: import numpy as np A = np.array([[.8, .6], [.1, 0]]) B1 = tf.keras.utils.normalize(A, axis=0, order=1) B2 = tf.keras.utils.normalize(A, axis=0, order=2) print('A:') print(A) print('B1:') print(B1) print('B2:') print(B2) which returns A: [[0.8 0.6] [0.1 0. ]] B1: [[0.88888889 1. ] [0.11111111 0. ]] B2: [[0.99227788 1. ] [0.12403473 0. ]] I understand how B1 is computed via order=1 such that each entry in A is divided by the sum of the elements in its column. For

问题 I have several time-based datasets which are of very different scale, e. g. [set 1] 2010-01-01 10 2010-02-01 12 2010-03-01 13 2010-04-01 19 … [set 2] 2010-01-01 920 2010-02-01 997 2010-03-01 1010 2010-04-01 1043 … I'd like to plot the relative growth of both since 2010-01-01. To put both curves on the same graph I have to normalize them. So I basically need to pick the first Y value and use it as a weight: plot "./set1" using 1:($2/10), "./set2" using 1:($2/920) But I want to do it

问题 Batch Normalization has different behavior in training phase and testing phase. For example, when using tf.contrib.layers.batch_norm in tensorflow, we should set different value for is_training in different phase. My qusetion is: what if I still set is_training=True when testing? That is to say what if I still use the training mode in testing phase? The reason why I come up with this question is that, the released code of both Pix2Pix and DualGAN don't set is_training=False when testing. And

问题 Batch Normalization has different behavior in training phase and testing phase. For example, when using tf.contrib.layers.batch_norm in tensorflow, we should set different value for is_training in different phase. My qusetion is: what if I still set is_training=True when testing? That is to say what if I still use the training mode in testing phase? The reason why I come up with this question is that, the released code of both Pix2Pix and DualGAN don't set is_training=False when testing. And

问题 Is it possible to normalize this table in R based on the last column(samples) samples = number of sequenced genomes. So I want to get a normalised distribution of all the genes in all the conditions. Simplified example of my data: I tried: dat1 <- read.table(text = " gene1 gene2 gene3 samples condition1 1 1 8 120 condition2 18 4 1 118 condition3 0 0 1 75 condition4 32 1 1 130", header = TRUE) dat1<-normalize(dat1, method = "standardize", range = c(0, 1), margin = 1L, on.constant = "quiet")