From dc7bcefc75ce66017b9bc1c504f2ef1dbc16fb5e Mon Sep 17 00:00:00 2001
From: Enzo De Carvalho Bittencourt <enzo.decarvalhobittencourt@ensiie.eu>
Date: Thu, 28 Sep 2023 16:32:15 +0200
Subject: [PATCH] updated stats
---
Random/__pycache__/stats.cpython-311.pyc | Bin 0 -> 1115 bytes
Random/main.py | 31 +++++++++++++++++++++++
Random/stats.py | 19 ++++++++++++++
3 files changed, 50 insertions(+)
create mode 100644 Random/__pycache__/stats.cpython-311.pyc
create mode 100644 Random/stats.py
diff --git a/Random/__pycache__/stats.cpython-311.pyc b/Random/__pycache__/stats.cpython-311.pyc
new file mode 100644
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diff --git a/Random/main.py b/Random/main.py
index 1d4c004..4af1704 100755
--- a/Random/main.py
+++ b/Random/main.py
@@ -3,7 +3,10 @@ import graph
from datetime import datetime
import matplotlib.pyplot as plt
import numpy as np
+import stats
+import scipy
import seaborn as sns
+import csv
#shut up stupid warnings from not "the-most-up-to-date" libraries
import warnings
@@ -43,6 +46,31 @@ def iteratives(N, seed, generator):
results[name] = res
return results
+def write_stats(data, name):
+ fields=["name","quantil25", "quantil50", "quantil75", "mean"
+ , "variance", "skew", "exckurt"]
+ quantil25 = np.quantile(data,0.25)
+ quantil50 = np.quantile(data,0.5)
+ quantil75 = np.quantile(data,0.75)
+ mean = np.mean(data)
+ variance = np.var(data)
+ skew = scipy.stats.skew(data)
+ exckurt = scipy.stats.kurtosis(data) - 3
+ #write file
+ with open(name+'.csv', 'w', newline='') as csvfile:
+ datawriter = csv.DictWriter(csvfile, fieldnames=fields)
+ datawriter.writeheader()
+ datawriter.writerow({
+ "name" : name,
+ "quantil25" : str(quantil25),
+ "quantil50" : str(quantil50),
+ "quantil75" : str(quantil75),
+ "mean" : str(mean),
+ "variance" : str(variance),
+ "skew" : str(skew),
+ "exckurt" : str(exckurt)})
+ return
+
if __name__=="__main__":
##INIT PARAMS##
N = 100
@@ -70,3 +98,6 @@ if __name__=="__main__":
graph.compare(0, RESOLUTION, seed
, iteratives([1000, 10, 100], seed, ParkMiller()))
+ print(stats.chisquared_uniform(data["ParkMiller"],N))
+ print(np.quantile(data["ParkMiller"],0.25))
+ write_stats(data["ParkMiller"],"ParkMiller")
diff --git a/Random/stats.py b/Random/stats.py
new file mode 100644
index 0000000..6b1b11d
--- /dev/null
+++ b/Random/stats.py
@@ -0,0 +1,19 @@
+"""
+Module to calculate various value on an array
+(Quantile, mean, etc..)
+"""
+import numpy as np
+import scipy
+
+def chisquared_uniform(arr, N):
+ """
+ chisquared for uniform distrib
+ """
+ _,counts = np.unique(arr, return_counts=True)
+ observed_freq = counts/N
+ #print(observed_freq)
+ expected_freq = np.full(shape=observed_freq.size,fill_value=1/N)
+ print(observed_freq),print(expected_freq)
+ print(np.sum(expected_freq), np.sum(observed_freq))
+ return scipy.stats.chisquare(observed_freq, expected_freq)
+
--
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