data-visualization

问题 Just contextualizing, I work with psychometrics/psychological testing. I have a dataset formed of "points","percentile","z_real","z_normal","iq". I would like to have a single ggplot in which I could present the Z_score (from my raw data), the z_score (with an underlying normal distribution), and then have two supplementary x-axes with "raw score" and "iq scores". That's pretty common in statistics, as you can check it below This is the current plot This is the best solution I've got That's

问题 Just contextualizing, I work with psychometrics/psychological testing. I have a dataset formed of "points","percentile","z_real","z_normal","iq". I would like to have a single ggplot in which I could present the Z_score (from my raw data), the z_score (with an underlying normal distribution), and then have two supplementary x-axes with "raw score" and "iq scores". That's pretty common in statistics, as you can check it below This is the current plot This is the best solution I've got That's

问题 Is the above visualization a rendering of the weights of the first convolutional layer or the activations on a given input image on the first convolutional layer? Below is a visualization of the weights of the first convolutional layer of the Inception v2 model that I've been training for 48 hours: I'm sure I have not converged my model after only 48 hours (on a CPU). Shouldn't those weights begin to smooth out by now, where training accuracy is over 90%? 回答1: According to ImageNet

问题 Is the above visualization a rendering of the weights of the first convolutional layer or the activations on a given input image on the first convolutional layer? Below is a visualization of the weights of the first convolutional layer of the Inception v2 model that I've been training for 48 hours: I'm sure I have not converged my model after only 48 hours (on a CPU). Shouldn't those weights begin to smooth out by now, where training accuracy is over 90%? 回答1: According to ImageNet

问题 I am using the R programming language. I am trying to learn how to plot "non linear decision boundaries", such as the examples seen over here: https://stats.stackexchange.com/questions/212965/how-to-achieve-a-nonlinear-decision-boundary . I am trying to learn how to do this both in 2 Dimensions and in 3 Dimensions. I have figured out how to make the basic plots, but I am still struggling to plot the decision boundaries. To illustrate this example, I have simulated some data. I then split this

问题 I am trying to learn how to deal with time series data. I created some fake daily data, tried to aggregate it by week and then plot it: set.seed(123) library(xts) library(ggplot2) date_decision_made = seq(as.Date("2014/1/1"), as.Date("2016/1/1"),by="day") date_decision_made <- format(as.Date(date_decision_made), "%Y/%m/%d") property_damages_in_dollars <- rnorm(731,100,10) final_data <- data.frame(date_decision_made, property_damages_in_dollars) y.mon<-aggregate(property_damages_in_dollars

问题 I am trying to learn how to deal with time series data. I created some fake daily data, tried to aggregate it by week and then plot it: set.seed(123) library(xts) library(ggplot2) date_decision_made = seq(as.Date("2014/1/1"), as.Date("2016/1/1"),by="day") date_decision_made <- format(as.Date(date_decision_made), "%Y/%m/%d") property_damages_in_dollars <- rnorm(731,100,10) final_data <- data.frame(date_decision_made, property_damages_in_dollars) y.mon<-aggregate(property_damages_in_dollars

问题 I know how to do this in R and have provided a code for it below. I want to know how can I do something similar to the below mentioned in Python Matplotlib or using any other library library(ggplot2) ggplot(dia[1:768,], aes(x = Glucose, fill = Outcome)) + geom_bar() + ggtitle("Glucose") + xlab("Glucose") + ylab("Total Count") + labs(fill = "Outcome") 回答1: Please consider the following example, which uses seaborn 0.11.1. import pandas as pd import numpy as np import seaborn as sns import

问题 I know how to do this in R and have provided a code for it below. I want to know how can I do something similar to the below mentioned in Python Matplotlib or using any other library library(ggplot2) ggplot(dia[1:768,], aes(x = Glucose, fill = Outcome)) + geom_bar() + ggtitle("Glucose") + xlab("Glucose") + ylab("Total Count") + labs(fill = "Outcome") 回答1: Please consider the following example, which uses seaborn 0.11.1. import pandas as pd import numpy as np import seaborn as sns import

问题 I am using the R programming language. I trying to learn how to customize hover text in 3d plotly objects as seen here: https://rstudio-pubs-static.s3.amazonaws.com/441420_9a7c15988f3c4f59b2d828eb87ba1634.html Recently, I have learned how to create a 3d plotly object for some data that I simulated : library(Rtsne) library(dplyr) library(ggplot2) library(plotly) library(caret) library(randomForest) #data a = iris a <- unique(a) #create two species just to make things easier s <- c("a","b")