I have a sparse matrix of count data that I'm using as input to a neural network. I know, usually, the input data should be normalized (e.g. via min-max scaling, $z$-score standardization, etc.). But for features that are counts, what is a good approach? Should I $\log_2(x+1)$ transform the data and then do a $z$-score standardization? Is there another better approach?

I am still trying to define this question precisely, so please indicate any feedback and I will edit my question. I have $M$ alternative models that need to be compared. The only measure that needs to be taken into account is a positive value $n$ that indicates how many independent sub-items (independent and with same weight on the total value) are supported by the data. The total number of items $N$ is not fixed, but it needs to be taken …

I want to check/experiment efficiency improvement of clustering algorithm under the title of Statistical preprocessing was done by including statistical frequency (counts) into dataframe concerning similar/same records. According to this paper: Statistical preprocessing is mainly used to get the frequency of samples having the same features, which are then used as inputs of the DBSCAN algorithm to improve the efficiency of DBSCAN clustering. Statistical preprocessing counts repeated samples with the same features in the URL parameter and uses the statistics …

I am trying to group my data by the 'ID' column. Then I want to count the frequency of 'sequence' for each 'ID'. Here is a sample of the data frame: ID Sequence 101 1-2 101 3-1 101 1-2 102 4-6 102 7-8 102 4-6 102 4-6 103 1118-69 104 1-2 104 1-2 I am looking for a count same as: ID Sequence Count 101 1-2 2 3-1 1 102 4-6 3 7-8 1 103 1118-69 1 104 1-2 2 …

For processes of discrete events occurring in continuous time with time-independent rate, we can use count models like Poisson or Negative Binomial. For discrete events that can occur once per sample in continuous time, with a time-dependent rate, we have survival models like Cox Proportional Hazards. What can we use for discrete event data in continuous time where there is an explicit time-dependence that we want to learn? I understand that sometimes people use sequential models where each node is …

Given three set of data with categorical integer x-axis with the same range (0-10): from itertools import chain from collections import Counter, defaultdict from IPython.display import Image import pandas as pd import numpy as np import seaborn as sns import colorlover as cl import matplotlib.pyplot as plt data1 = Counter({8: 10576, 9: 10114, 7: 9504, 6: 7331, 10: 6845, 5: 5007, 4: 3037, 3: 1792, 2: 908, 1: 368, 0: 158}) data2 = Counter({5: 9030, 6: 8347, 4: 8149, 7: …

I'm currently starting out in R and wondering how to count the number of observations per day, per node, per replicate from the below dataset, and store in a different data set. The original dataset looks like this: Would like the resulting dataset to look like this: Can someone help me find out how I could do this in R? Thanks

I have a stack of ATM cards and I want to count the number of cards available in the stack. How to proceed through it? I'm using Python 3.6.0 and opencv2. I'm attaching PNG file of the images. Kindly provide help in this direction.[1

Question Is Poisson model the best method for predicting counts among multiple levels within nominal variable? Details Imagine data of 7000 observations, where output= Obs.Count {numeric,0,1,2..8} and features=location {factor, 13 levels} . When conducting Poisson regression, the output returns: ## function for glm #p1 <- glm(Count ~ Loc,family = poisson, data = dat) Call: glm(formula = Count ~ Loc, family = "poisson", data = p1) Deviance Residuals: Min 1Q Median 3Q Max -2.49116 -1.32852 0.00775 1.02579 1.55985 Coefficients: Estimate Std. …

I am evaluating whether governance predictor variables are associated with the prevalence of groundwater fecal contamination in a developing country context, as measured by TTC (Thermotolerant Coliform) counts per 100mL of water. In my data TTC is distributed non-normally. There are many zeroes, and also many water sources with TTC of 125+ (our test kits could not measure TTC above this threshold). I ran countfit on TTC and various predictors and it appeared to indicated ZINBR was the appropriate regression …

I have an ngram-based language model that produces a long tag list for a given sentence. For example, the just-previous sentence, broken into bigrams, and run through the model might produce something like: {I have}=>C1 {have an}=>C2 {an ngram}=>C1 {ngram based}=>C3, etc. resulting in counts: C1=2, C2=1, C3=1 (for the shown segment above). Easy enough to pick the winner by sorting either the counts, or after turning them into percentages, which would control for sentence length. But I want a …

How do I get from a dataframe with multiple columns that have similar values and need to be merged: df1 = pd.DataFrame({'firstcolumn':['ab', 'ca', 'da', 'ta','la'], 'secondcolumn':['ab', 'ca', 'ta', 'da', 'sa'], 'index':[2011,2012,2011,2012,2012]}) To a crosstab that tells me for each year how many values were collected? Index ab ca da ta sa la 2011 2 0 1 1 0 0 2012 0 2 1 1 1 1 Also, how could then plot the table?