addded r

age_sex_heart_attack.csv

@@ -0,0 +1,21 @@
+age,sex,heart attack,no heart attack
+"[30, 35]",0,1.0,0.0
+"[30, 35]",1,1.0,0.0
+"[35, 40]",0,0.8495212038303693,0.15047879616963064
+"[35, 40]",1,0.6055487402094931,0.39445125979050677
+"[40, 45]",0,0.8827292110874201,0.11727078891257994
+"[40, 45]",1,0.6717964824120602,0.3282035175879397
+"[45, 50]",0,0.8070175438596491,0.19298245614035084
+"[45, 50]",1,0.532089552238806,0.467910447761194
+"[50, 55]",0,0.8411934552454283,0.1588065447545717
+"[50, 55]",1,0.5902317880794702,0.40976821192052987
+"[55, 60]",0,0.6172962226640158,0.3827037773359841
+"[55, 60]",1,0.3048890578229676,0.6951109421770323
+"[60, 65]",0,0.5713589868485144,0.4286410131514856
+"[60, 65]",1,0.26603893680999097,0.7339610631900091
+"[65, 70]",0,0.7016949152542373,0.29830508474576267
+"[65, 70]",1,0.39011490060186027,0.6098850993981397
+"[70, 75]",0,0.8070175438596491,0.19298245614035084
+"[70, 75]",1,0.532089552238806,0.467910447761194
+"[75, 80]",0,0.7150259067357513,0.2849740932642487
+"[75, 80]",1,0.4055745164960181,0.5944254835039817

charts.R

@@ -0,0 +1,36 @@
+basedf<-read.csv("dev/matma_bayes/heart.csv", header = TRUE)
+siha<-read.csv('dev/matma_bayes/sex_if_heart_attack.csv')
+df2<-read.csv('dev/matma_bayes/age_sex_heart_attack.csv')
+basedf$sex[basedf$sex==1]<-'Man'
+basedf$sex[basedf$sex==0]<-'Woman'
+df2$sex[df2$sex==1]<-'Man'
+df2$sex[df2$sex==0]<-'Woman'
+df2$x<-paste(df2$sex,df2$age)
+df2$comb<-
+
+basedf%>%
+  count(sex)%>% arrange(n)%>%
+  hchart(type = "pie", hcaes(x=sex, y = n, color = n), name="Quantity") %>%
+  hc_title(text="<b>Sex in dataset</b>")
+
+
+siha%>%
+  hchart(type = "pie", hcaes(x=sex, y = Probability, color = Probability, name='Probability')) %>%
+  hc_title(text="<b>Sex if heart attack</b>")
+
+basedf%>%
+  group_by(sex, chol)%>%
+  count()%>%
+  hchart('area', hcaes(x='chol', y='n', group='sex'))%>%
+  hc_xAxis(title=list(text='Cholesterol'))%>%
+  hc_yAxis(title=list(text='Quantity'))%>%
+  hc_title(text="<b>Number of terrorists attacks in each year by region</b>")
+
+
+df2%>%
+  hchart('column', hcaes(x=x, y=heart.attack, group=age))%>%
+  hc_plotOptions(column = list(stacking = "normal"))%>%
+  hc_xAxis(title=list(text='Age'))%>%
+  hc_yAxis(title=list(text='Probability'))%>%
+  hc_legend(enabled=F)
+  hc_title(text="<b>Probability of heart attack for sex in age group</b>")

main.py

@@ -1,93 +1,92 @@
 import pandas as pd
-
-data=pd.read_csv('heart.csv')
-
-print(data)
-
-men=0
-for i in range(len(data['sex'])):
-    if data['sex'][i] ==1:
-        men+=1
-print(men)
-p_men=men/len(data)
-print("ssssss",p_men)
-
-chol=0
-for i in range(len(data['chol'])):
-    if data['chol'][i] >200:
-        chol+=1
-print(chol)
-p_chol=chol/len(data)
-print(p_chol)
-
-age_over_50=0
-for i in range(len(data['age'])):
-    if data['age'][i] >50:
-        age_over_50+=1
-print(age_over_50)
-p_age_over_50=age_over_50/len(data)
-print(p_age_over_50)
-
-sugar=0
-for i in range(len(data['fbs'])):
-    if data['fbs'][i] ==1:
-        sugar+=1
-print(sugar)
-p_sugar=sugar/len(data)
-print(p_sugar)
-
-heart_attack=0
-for i in range(len(data['target'])):
-    if data['target'][i] ==1:
-        heart_attack+=1
-print(heart_attack)
-p_heart_attack=heart_attack/len(data)
-print(p_heart_attack)   #P(class)
-
-man_if_heart_attack=0
-for i in range(len(data['target'])):
-    if data['target'][i] ==1:
-        if data['sex'][i]==1:
-            man_if_heart_attack+=1
-print(man_if_heart_attack)
-p_man_if_heart_attack=man_if_heart_attack/len(data)
-p_man_if_heart_attack=p_man_if_heart_attack/p_heart_attack
-print(p_man_if_heart_attack)
-
-over50_if_heart_attack=0
-for i in range(len(data['target'])):
-    if data['target'][i] ==1:
-        if data['age'][i]>50:
-            over50_if_heart_attack+=1
-print(over50_if_heart_attack)
-p_over50_if_heart_attack=over50_if_heart_attack/len(data)
-p_over50_if_heart_attack=p_over50_if_heart_attack/p_heart_attack
-print(p_over50_if_heart_attack)
-
-chol_over200_if_heart_attack=0
-for i in range(len(data['target'])):
-    if data['target'][i] ==1:
-        if data['chol'][i]>200:
-            chol_over200_if_heart_attack+=1
-print(chol_over200_if_heart_attack)
-p_chol_over200_if_heart_attack=chol_over200_if_heart_attack/len(data)
-p_chol_over200_if_heart_attack=p_chol_over200_if_heart_attack/p_heart_attack
-print(p_chol_over200_if_heart_attack)
-
-sugar_if_heart_attack=0
-for i in range(len(data['target'])):
-    if data['target'][i] ==1:
-        if data['fbs'][i]==1:
-            sugar_if_heart_attack+=1
-print(sugar_if_heart_attack)
-p_sugar_if_heart_attack=sugar_if_heart_attack/len(data)
-p_sugar_if_heart_attack=p_sugar_if_heart_attack/p_heart_attack
-print(p_sugar_if_heart_attack)
+import numpy
+import csv
+data = pd.read_csv('heart.csv')
 
 
-licznik=p_man_if_heart_attack*p_sugar_if_heart_attack*p_over50_if_heart_attack*p_chol_over200_if_heart_attack*p_heart_attack
-mianownik=(p_man_if_heart_attack*p_sugar_if_heart_attack*p_over50_if_heart_attack*p_chol_over200_if_heart_attack*p_heart_attack)+((1-p_man_if_heart_attack)*(1-p_sugar_if_heart_attack)*(1-p_over50_if_heart_attack)*(1-p_chol_over200_if_heart_attack)*(1-p_heart_attack))
+def p_target(target):
+    count = 0
+    for i in data['target']:
+        if i == target:
+            count += 1
+    return count / len(data)
+
+
+def p_property(property_name, value):
+    count = 0
+    if value == 0 or value == 1:
+        for i in data[property_name]:
+            if i == value:
+                count += 1
+    else:
+        for i in data[property_name]:
+            if value[0] <= i and i < value[1]:
+                count += 1
+    return count / len(data)
+
+
+def p_property_if_target(property_name, property_value, target):
+    property_if_target = 0
+    count_target = 0
+    for i in range(len(data['target'])):
+        if data['target'][i] == target:
+            count_target += 1
+            if property_value == 0 or property_value == 1:
+                if data[property_name][i] == property_value:
+                    property_if_target += 1
+            else:
+                if property_value[0] <= data[property_name][i] and data[property_name][i] < property_value[1]:
+                    property_if_target += 1
+    return property_if_target / count_target
+
+
+def p_target_if_property(properties_values, target):
+    p_properties_if_target = []
+    for p in properties_values:
+        p_properties_if_target.append(p_property_if_target(p[0], p[1], target))
+    numerator = numpy.prod(p_properties_if_target) * p_target(target)
+    print(numerator)
+    p_properties_if_target = [[], []]
+    for t in [0, 1]:
+        for p in properties_values:
+            p_properties_if_target[t].append(p_property_if_target(p[0], p[1], t))
+    denominator = sum([numpy.prod(p_properties_if_target[t]) * p_target(t) for t in [0, 1]])  # coś tutaj jest nie tak
+    print(denominator)
+    return numerator / denominator
+
+
+print(p_property('sex', value=1), p_property('sex', value=0))
+print(p_property('chol', value=[200, 1000]))
+print(p_property('age', value=[50, 1000]))
+print(p_property('fbs', value=1))
+print(p_property('target', value=1))
+
+print(p_property_if_target('sex', 1, 1))
+print(p_property_if_target('age', [51, 1000], 1))
+print(p_property_if_target('chol', [201, 1000], 1))
+print(p_property_if_target('fbs', 1, 1))
+
+print()
+
+print(p_target_if_property([('sex', 1), ('chol', [201, 1000]), ('age', [51, 120]), ('fbs', 1)], 1))
+
+
+print(p_property_if_target('sex', 1, 1))
+print(p_property_if_target('sex', 0, 1))
+d={'sex':['Woman', 'Man'], 'Probability': [p_property_if_target('sex', 0, 1), p_property_if_target('sex', 1, 1)]}
+p=pd.DataFrame(d)
+
+pd.DataFrame(p).to_csv('sex_if_heart_attack.csv',index=False, header=True)
+
+age_sex_target = []
+for a in range(30, 80, 5):
+    print(a, a+5)
+    for i in [0,1]:
+        age_sex_target.append([[a, a + 5], i, p_target_if_property([('sex', i), ('age', [a, a + 5])], 1), p_target_if_property([('sex', i), ('age', [a, a + 5])], 0)])
+
+with open('age_sex_heart_attack.csv', 'w', newline='') as file:
+    writer = csv.writer(file)
+    writer.writerow(["age", "sex", "heart attack", "no heart attack"])
+    for i in age_sex_target:
+        writer.writerow(i)
 
-print(licznik)
-print(mianownik)
-print(licznik/mianownik)

sex_if_heart_attack.csv

@@ -0,0 +1,3 @@
+sex,Probability
+Woman,0.43636363636363634
+Man,0.5636363636363636