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Is there any way to detect the highest peaks using a python library without setting any parameter?. I'm developing a user interface and I want the algorithm to be able to detect highest peaks automatically...
I want it to be able to detect these peaks in picture below:
graph here
Data looks like this:
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inf
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The task you are describing could be treated like anomaly/outlier detection.
One possible solution is to use a Z-score transformation and treat every value with a z score above a certain threshold as an outlier. Because there is no clear definition of an outlier it won't be able to detect such peaks without setting any parameters (threshold).
One possible solution could be:
import numpy as np
def detect_outliers(data):
outliers = []
d_mean = np.mean(data)
d_std = np.std(data)
threshold = 3 # this defines what you would consider a peak (outlier)
for point in data:
z_score = (point - d_mean)/d_std
if np.abs(z_score) > threshold:
outliers.append(point)
return outliers
# create normal data
data = np.random.normal(size=100)
# create outliers
outliers = np.random.normal(100, size=3)
# combine normal data and outliers
full_data = data.tolist() + outliers.tolist()
# print outliers
print(detect_outliers(full_data))
If you only want to detect peaks, remove the np.abs function call from the code.
This code snippet is based on a Medium Post, which also provides another way of detecting outliers.
So I am trying to solve mean, median and mode challenge on Hackerrank. I defined 3 functions to calculate mean, median and mode for a given array with length between 10 and 2500, inclusive.
I get an error with an array of 2500 integers, not sure why. I looked into python documentation and found no mentions of max length for lists. I know I can use statistics module but trying the hard way and being stubborn I guess. Any help and criticism is appreciated regarding my code. Please be honest and brutal if need be. Thanks
N = int(input())
var_list = [int(x) for x in input().split()]
def mean(sample_list):
mean = sum(sample_list)/N
print(mean)
return
def median(sample_list):
sorted_list = sorted(sample_list)
if N%2 != 0:
median = sorted_list[(N//2)]
else:
median = (sorted_list[N//2] + sorted_list[(N//2)-1])/2
print(median)
return
def mode(sample_list):
sorted_list = sorted(sample_list)
mode = min(sorted_list)
max_count = sorted_list.count(mode)
for i in sorted_list:
if (i <= mode) and (sorted_list.count(i) >= max_count):
mode = i
print(mode)
return
mean(var_list)
median(var_list)
mode(var_list)
Compiler Message
Wrong Answer
Input (stdin)
2500
19325 74348 68955 98497 26622 32516 97390 64601 64410 10205 5173 25044 23966 60492 71098 13852 27371 40577 74997 42548 95799 26783 51505 25284 49987 99134 33865 25198 24497 19837 53534 44961 93979 76075 57999 93564 71865 90141 5736 54600 58914 72031 78758 30015 21729 57992 35083 33079 6932 96145 73623 55226 18447 15526 41033 46267 52486 64081 3705 51675 97470 64777 31060 90341 55108 77695 16588 64492 21642 56200 48312 5279 15252 20428 57224 38086 19494 57178 49084 37239 32317 68884 98127 79085 77820 2664 37698 84039 63449 63987 20771 3946 862 1311 77463 19216 57974 73012 78016 9412 90919 40744 24322 68755 59072 57407 4026 15452 82125 91125 99024 49150 90465 62477 30556 39943 44421 68568 31056 66870 63203 43521 78523 58464 38319 30682 77207 86684 44876 81896 58623 24624 14808 73395 92533 4398 8767 72743 1999 6507 49353 81676 71188 78019 88429 68320 59395 95307 95770 32034 57015 26439 2878 40394 33748 41552 64939 49762 71841 40393 38293 48853 81628 52111 49934 74061 98537 83075 83920 42792 96943 3357 83393{-truncated-}
Download to view the full testcase
Expected Output
49921.5
49253.5
2184
Your issue seems to be that you are actually using standard list operations rather than calculating things on the fly, while looping through the data once (for the average). sum(sample_list) will almost surely give you something which exceeds the double-limit, i.a.w. it becomes really big.
Further reading
Calculating the mean, variance, skewness, and kurtosis on the fly
How do I determine the standard deviation (stddev) of a set of values?
Rolling variance algorithm
What is a good solution for calculating an average where the sum of all values exceeds a double's limits?
How do I determine the standard deviation (stddev) of a set of values?
How to efficiently compute average on the fly (moving average)?
I figured out that you forgot to change the max_count variable inside the if block. Probably that causes the wrong result. I tested the debugged version on my computer and they seem to work well when I compare their result with the scipy's built-in functions. The correct mode function should be
def mode(sample_list):
N = len(sample_list)
sorted_list = sorted(sample_list)
mode = min(sorted_list)
max_count = sorted_list.count(mode)
for i in sorted_list:
if (sorted_list.count(i) >= max_count):
mode = i
max_count = sorted_list.count(i)
print(mode)
I was busy with some stuff and now came back to completing this. I am happy to say that I have matured enough as a coder and solved this issue.
Here is the solution:
# Enter your code here. Read input from STDIN. Print output to STDOUT
# Input an array of numbers, convert it to integer array
n = int(input())
my_array = list(map(int, input().split()))
my_array.sort()
# Find mean
array_mean = sum(my_array) / n
print(array_mean)
# Find median
if (n%2) != 0:
array_median = my_array[n//2]
else:
array_median = (my_array[n//2 - 1] + my_array[n//2]) / 2
print(array_median)
# Find mode(I could do this using multimode method of statistics module for python 3.8)
def sort_second(array):
return array[1]
modes = [[i, my_array.count(i)] for i in my_array]
modes.sort(key = sort_second, reverse=True)
array_mode = modes[0][0]
print(array_mode)
In my GUI I am using this matlab code to store the values in excel sheet.This code is calculating the glcm six features.
function [Contrast,cor,ener,homo,Var,Entropy] = glcm_feature_extraction(I1)
Contrast = graycoprops(graycomatrix(rgb2gray(I1)),'Contrast')
cor= graycoprops(graycomatrix(rgb2gray(I1)), 'Correlation')
ener = graycoprops(graycomatrix(rgb2gray(I1)), 'Energy')
homo = graycoprops(graycomatrix(rgb2gray(I1)), 'Homogeneity')
img = double(I1);
Var = var((img(:)))
Entropy=entropy(I1)
Here suppose I get these values after calculation:
[0.603606103 : 0.785092239 : 0.271728411 : 0.855429408 :1889.578963 : 7.178149206]
But iI need only approx value like:
[0.6 : 0.7 : .2 ....]
How to modify this code to get this result?
For example, lets consider Contrast=0.603606103
And you wanted to make approximately as 0.6 then use the following :
sprintf('%.1f',Contrast);
which should give you result exactly Contrast=0.6
Similarly do it for all remaining 5 variables.
Correct me if I'm wrong: the "thresholds" returned by scikit-learn's roc_curve should be an array of numbers that are in [0,1]. However, it sometimes gives me an array with the first number close to "2". Is it a bug or I did sth wrong? Thanks.
In [1]: import numpy as np
In [2]: from sklearn.metrics import roc_curve
In [3]: np.random.seed(11)
In [4]: aa = np.random.choice([True, False],100)
In [5]: bb = np.random.uniform(0,1,100)
In [6]: fpr,tpr,thresholds = roc_curve(aa,bb)
In [7]: thresholds
Out[7]:
array([ 1.97396826, 0.97396826, 0.9711752 , 0.95996265, 0.95744405,
0.94983331, 0.93290463, 0.93241372, 0.93214862, 0.93076592,
0.92960511, 0.92245024, 0.91179548, 0.91112166, 0.87529458,
0.84493853, 0.84068543, 0.83303741, 0.82565223, 0.81096657,
0.80656679, 0.79387241, 0.77054807, 0.76763223, 0.7644911 ,
0.75964947, 0.73995152, 0.73825262, 0.73466772, 0.73421299,
0.73282534, 0.72391126, 0.71296292, 0.70930102, 0.70116428,
0.69606617, 0.65869235, 0.65670881, 0.65261474, 0.6487222 ,
0.64805644, 0.64221486, 0.62699782, 0.62522484, 0.62283401,
0.61601839, 0.611632 , 0.59548669, 0.57555854, 0.56828967,
0.55652111, 0.55063947, 0.53885029, 0.53369398, 0.52157349,
0.51900774, 0.50547317, 0.49749635, 0.493913 , 0.46154029,
0.45275916, 0.44777116, 0.43822067, 0.43795921, 0.43624093,
0.42039077, 0.41866343, 0.41550367, 0.40032843, 0.36761763,
0.36642721, 0.36567017, 0.36148354, 0.35843793, 0.34371331,
0.33436415, 0.33408289, 0.33387442, 0.31887024, 0.31818719,
0.31367915, 0.30216469, 0.30097917, 0.29995201, 0.28604467,
0.26930354, 0.2383461 , 0.22803687, 0.21800338, 0.19301808,
0.16902881, 0.1688173 , 0.14491946, 0.13648451, 0.12704826,
0.09141459, 0.08569481, 0.07500199, 0.06288762, 0.02073298,
0.01934336])
Most of the time these thresholds are not used, for example in calculating the area under the curve, or plotting the False Positive Rate against the True Positive Rate.
Yet to plot what looks like a reasonable curve, one needs to have a threshold that incorporates 0 data points. Since Scikit-Learn's ROC curve function need not have normalised probabilities for thresholds (any score is fine), setting this point's threshold to 1 isn't sufficient; setting it to inf is sensible but coders often expect finite data (and it's possible the implementation also works for integer thresholds). Instead the implementation uses max(score) + epsilon where epsilon = 1. This may be cosmetically deficient, but you haven't given any reason why it's a problem!
From the documentation:
thresholds : array, shape = [n_thresholds]
Decreasing thresholds on the decision function used to compute
fpr and tpr. thresholds[0] represents no instances being predicted
and is arbitrarily set to max(y_score) + 1.
So the first element of thresholds is close to 2 because it is max(y_score) + 1, in your case thresholds[1] + 1.
this seems like a bug to me - in roc_curve(aa,bb), 1 is added to the first threshold. You should create an issue here https://github.com/scikit-learn/scikit-learn/issues
I want to plot a simplified heatmap that is not so difficult to edit with the scalar vector graphics program I am using (inkscape). The original heatmap as produced below contains lots of rectangles, and I wonder if they could be merged together in the different sectors to simplify the output pdf file:
nentries=100000
ci=rainbow(nentries)
set.seed=1
mean=10
## Generate some data (4 factors)
i = data.frame(
a=round(abs(rnorm(nentries,mean-2))),
b=round(abs(rnorm(nentries,mean-1))),
c=round(abs(rnorm(nentries,mean+1))),
d=round(abs(rnorm(nentries,mean+2)))
)
minvalue = 10
# Discretise values to 1 or 0
m0 = matrix(as.numeric(i>minvalue),nrow=nrow(i))
# Remove rows with all zeros
m = m0[rowSums(m0)>0,]
# Reorder with 1,1,1,1 on top
ms =m[order(as.vector(m %*% matrix(2^((ncol(m)-1):0),ncol=1)), decreasing=TRUE),]
rowci = rainbow(nrow(ms))
colci = rainbow(ncol(ms))
colnames(ms)=LETTERS[1:4]
limits=c(which(!duplicated(ms)),nrow(ms))
l=length(limits)
toname=round((limits[-l]+ limits[-1])/2)
freq=(limits[-1]-limits[-l])/nrow(ms)
rn=rep("", nrow(ms))
for(i in toname) rn[i]=paste(colnames(ms)[which(ms[i,]==1)],collapse="")
rn[toname]=paste(rn[toname], ": ", sprintf( "%.5f", freq ), "%")
heatmap(ms,
Rowv=NA,
labRow=rn,
keep.dendro = FALSE,
col=c("black","red"),
RowSideColors=rowci,
ColSideColors=colci,
)
dev.copy2pdf(file="/tmp/file.pdf")
Why don't you try RSvgDevice? Using it you could save your image as svg file, which is much convenient to Inkscape than pdf
I use the Cairo package for producing svg. It's incredibly easy. Here is a much simpler plot than the one you have in your example:
require(Cairo)
CairoSVG(file = "tmp.svg", width = 6, height = 6)
plot(1:10)
dev.off()
Upon opening in Inkscape, you can ungroup the elements and edit as you like.
Example (point moved, swirl added):
I don't think we (the internet) are being clear enough on this one.
Let me just start off with a successful export example
png("heatmap.png") #Ruby dev's think of this as kind of like opening a `File.open("asdfsd") do |f|` block
heatmap(sample_matrix, Rowv=NA, Colv=NA, col=terrain.colors(256), scale="column", margins=c(5,10))
dev.off()
The dev.off() bit, in my mind, reminds me of an end call to a ruby block or method, in that, the last line of the "nested" or enclosed (between png() and dev.off()) code's output is what gets dumped into the png file.
For example, if you ran this code:
png("heatmap4.png")
heatmap(sample_matrix, Rowv=NA, Colv=NA, col=terrain.colors(32), scale="column", margins=c(5,15))
heatmap(sample_matrix, Rowv=NA, Colv=NA, col=greenred(32), scale="column", margins=c(5,15))
dev.off()
it would output the 2nd (greenred color scheme, I just tested it) heatmap to the heatmap4.png file, just like how a ruby method returns its last line by default