Bayes

[dyasam]

#!/usr/bin/env python

coding: utf-8

In[2]:

###--start-- sinif-dictionary olusturulan ve sinifa deger atanan bolum
get_ipython().run_line_magic('matplotlib', 'inline')
def generate_data():

import math
import random
my_classes={}

kac_sinif_olsun=5
kac_deger_atansin=2000
araliklar_ne_olsun=[random.randint(5,50) for x in range(kac_sinif_olsun)]

for s in range(kac_sinif_olsun):
    class_name=str(s)+"_class"
    my_classes[class_name]={}
    aralik=araliklar_ne_olsun[s]
    my_classes[class_name]["data"]=[ random.random()*(aralik+s*5)+(s*5) for x in range(kac_deger_atansin)]

    #ortalama hesaplama ve sinifin "mean" alanina ekleme
    a=sum(my_classes[class_name]["data"])
    b=len(my_classes[class_name]["data"])    
    my_classes[class_name]["mean"]=a/b
    my_classes[class_name]["class_id"]=s


    #varyans ve standartsapma hesabi 1. satir (data-mean)^2 --- 2.satir newline_degerlertoplami/elemansayisi
    new_line=[(x-my_classes[class_name]["mean"])**2 for x in my_classes[class_name]["data"] ]
    my_var=sum(new_line)/(len(new_line)-1)

    my_classes[class_name]["var"]=math.floor(my_var)
    my_classes[class_name]["std"]=math.sqrt(my_var)

#my_classes.keys()
return my_classes
###--*end*-- sinif-dictionary olusturulan ve sinifa deger atanan bolum

generate_data()

#normal dagilim hesabi
def calculate_probability_one_value(x, mean, stdev):
import math
exponent = math.exp(-(math.pow(x-mean,2)/(2math.pow(stdev,2))))
return (1 / (math.sqrt(2math.pi) * stdev)) * exponent
def define_probability_for_data(data): # # _and_plot(data):

my_classes=data
kac_sinif_olsun=len(my_classes.keys())

for s in range(kac_sinif_olsun):
    class_name=str(s)+"_class"

    #sadece 0_class icin garfik cizimi
    mean=my_classes[class_name]["mean"]
    stdev=my_classes[class_name]["std"] 
    values_raw=my_classes[class_name]["data"]
    my_classes[class_name]["prob"]=[]
 
    for i in values_raw:
        my_classes[class_name]["prob"].append(calculate_probability_one_value(i,mean,stdev))
        
return my_classes

def plot_data(t,data):
d=data
import matplotlib.pyplot as plt
fig = plt.figure()
ax = plt.subplot(111)
for c in d.keys():
# plt.plot(d[c]['data'],d[c]['prob'],'.',label='cdfsdf')

    ax.plot(d[c]['data'],d[c]['prob'],'.',label=c)


r=calculate_test_probability(t,data)
x1=t
x2=t
y1=0
p_s=[y for (x,y) in r]
m=p_s.index(max(p_s))
print(" bu veri ",m," sınıfına ait ")
y2=r[m][1]
plt.plot([x1,x2],[y1,y2],"k")
plt.legend()
plt.show()

def calculate_test_probability(test_value,d):
test_value_probabilities=[]
for c in d.keys():
mean=d[c]['mean']
stdev=d[c]['std']
test_value_probabilities.append((c,calculate_probability_one_value(test_value, mean, stdev)))
return test_value_probabilities
def my_run(t):
data=generate_data()
data=define_probability_for_data(data)
calculate_test_probability(t,data)
plot_data(t,data)
return calculate_test_probability(50,data)

In[4]:

my_run(40)

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