histogram

I am very new to R, so I apologize for such a basic question. I spent an hour googling this issue, but couldn't find a solution. Say I have some categorical data in my data set about common pet types. I input it as a character vector in R that contains the names of different types of animals. I created it like this: animals <- c("cat", "dog", "dog", "dog", "dog", "dog", "dog", "dog", "cat", "cat", "bird") I turn it into a factor for use with other vectors in my data frame: animalFactor <- as.factor(animals) I now want to create a histogram that shows the frequency of each variable on the y

There are many ways to write a Python program that computes a histogram. By histogram, I mean a function that counts the occurrence of objects in an iterable and outputs the counts in a dictionary. For example: >>> L = 'abracadabra' >>> histogram(L) {'a': 5, 'b': 2, 'c': 1, 'd': 1, 'r': 2} One way to write this function is: def histogram(L): d = {} for x in L: if x in d: d[x] += 1 else: d[x] = 1 return d Are there more concise ways of writing this function? If we had dictionary comprehensions in Python, we could write: >>> { x: L.count(x) for x in set(L) } but since Python 2.6 doesn't have

For a small project, I need to compare one image with another - to determine if the images are approximately the same or not. The images are smallish, varying from 25 to 100px across. The images are meant to be of the same picture data but are sublty different, so a simple pixel equality check won't work. Consider these two possible scenarios: A security (CCTV) camera in a museum looking at an exhibit: we want to quickly see if two different video frames show the same scene, but slight differences in lighting and camera focus means they won't be identical. A picture of a vector computer GUI

It is really straightforward to see and understand the scalar values in TensorBoard. However, it's not clear how to understand histogram graphs. For example, they are the histograms of my network weights. (After fixing a bug thanks to sunside) What is the best way to interpret these? Layer 1 weights look mostly flat, what does this mean? I added the network construction code here. X = tf.placeholder(tf.float32, [None, input_size], name="input_x") x_image = tf.reshape(X, [-1, 6, 10, 1]) tf.summary.image('input', x_image, 4) # First layer of weights with tf.name_scope("layer1"): W1 = tf.get

I've got a 2-D histogram (the plot is 3D - several histograms graphed side by side) that I've generated with the bar3 plot command. However, all the zero values show up as flat squares in the x-y plane. Is there a way I can prevent MATLAB from displaying the values? I already tried replacing all zeros with NaNs, but it didn't change anything about the plot. Here's the code I've been experimenting with: x1=normrnd(50,15,100,1); %generate random data to test code x2=normrnd(40,13,100,1); x3=normrnd(65,12,100,1); low=min([x1;x2;x3]); high=max([x1;x2;x3]); y=linspace(low,high,(high-low)/4);

A ggplot2-challenged latticist needs help: What's the syntax to request variable per-facet breaks in a histogram? library(ggplot2) d = data.frame(x=c(rnorm(100,10,0.1),rnorm(100,20,0.1)),par=rep(letters[1:2],each=100)) # Note: breaks have different length by par breaks = list(a=seq(9,11,by=0.1),b=seq(19,21,by=0.2)) ggplot(d, aes(x=x) ) + geom_histogram() + ### Here the ~breaks should be added facet_wrap(~ par, scales="free") As pointed out by jucor , here some more solutions. On special request, and to show why I am not a great ggplot fan, the lattice version library(lattice) d = data.frame(x