minor fixes

charts.R

@@ -1,12 +1,19 @@
+library(highcharter)
+library(tidyr)
+library(plotly)
+library(dplyr)
 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')
+chol<-read.csv('dev/matma_bayes/chol_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'
+chol$sex[chol$sex==1]<-'Man'
+chol$sex[chol$sex==0]<-'Woman'
 df2$x<-paste(df2$sex,df2$age)
-df2$comb<-
+
 
 basedf%>%
   count(sex)%>% arrange(n)%>%
@@ -15,16 +22,10 @@ basedf%>%
 
 
 siha%>%
-  hchart(type = "pie", hcaes(x=sex, y = Probability, color = Probability, name='Probability')) %>%
+  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%>%
@@ -34,3 +35,20 @@ df2%>%
   hc_yAxis(title=list(text='Probability'))%>%
   hc_legend(enabled=F)%>%
   hc_title(text="<b>Probability of heart attack for sex and age group</b>")
+
+
+chol %>%
+  drop_na(heart.attack)%>%
+    plot_ly(x = ~heart.attack,
+            y = ~sex,
+            type = 'bar',
+            orientation = 'h',
+            color = ~chol,
+            colors ='Spectral',
+            hoverinfo = 'text',
+            text = ~paste('Probability:', heart.attack, '<br> Interval:', chol),
+          xaxis=list(categoryorder='total descending')) %>%
+ layout(title = 'Chol/probability',
+       xaxis = list(title = "Probability"),
+      yaxis = list(title = "Cholesterol"))
+

chol_sex_heart_attack.csv

@@ -0,0 +1,31 @@
+chol,sex,heart attack,no heart attack
+"[120, 150]",0,0.7900763358778626,0.20992366412213742
+"[120, 150]",1,0.5057933317569164,0.4942066682430835
+"[150, 180]",0,0.7150259067357513,0.28497409326424866
+"[150, 180]",1,0.40557451649601817,0.5944254835039818
+"[180, 210]",0,0.8168701442841287,0.18312985571587126
+"[180, 210]",1,0.5481189641089703,0.4518810358910296
+"[210, 240]",0,0.7900763358778626,0.2099236641221374
+"[210, 240]",1,0.5057933317569164,0.4942066682430835
+"[240, 270]",0,0.7594186538732892,0.2405813461267109
+"[240, 270]",1,0.46189495365602473,0.5381050463439754
+"[270, 300]",0,0.5654082528533801,0.43459174714661986
+"[270, 300]",1,0.26132942377673635,0.7386705762232636
+"[300, 330]",0,0.7553882996920972,0.24461170030790286
+"[300, 330]",1,0.45644780538550794,0.5435521946144921
+"[330, 360]",0,0.5564516129032259,0.4435483870967742
+"[330, 360]",1,0.2543703175169461,0.745629682483054
+"[360, 390]",0,1.0,0.0
+"[360, 390]",1,1.0,0.0
+"[390, 420]",0,0.7150259067357513,0.2849740932642487
+"[390, 420]",1,0.4055745164960181,0.5944254835039817
+"[420, 450]",0,nan,nan
+"[420, 450]",1,nan,nan
+"[450, 480]",0,nan,nan
+"[450, 480]",1,nan,nan
+"[480, 510]",0,nan,nan
+"[480, 510]",1,nan,nan
+"[510, 540]",0,nan,nan
+"[510, 540]",1,nan,nan
+"[540, 570]",0,1.0,0.0
+"[540, 570]",1,1.0,0.0

first.py

@@ -0,0 +1,91 @@
+import pandas as pd
+
+
+#       P(B|A)*P(A)
+#P(A|B)=-----------
+#          P(B)
+
+
+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("Prawdopodobieństwo, że mężczyzna",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("Prawdopodobieństwo, że cholesterol większy niż 200",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("Prawdopodobieństwo, że wiek powyżej 50",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("Prawdopodobieństwo, że wysoki cukier",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("Prawdopodobieństwo dużego ryzyka zawału serca",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("rawdopodobieństwo, że mężczyzna jeśli wystąpił zawał serca",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("Prawdopodobieństwo, że powyżej 50 lat jeżeli wystąpił zawał serca",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("Prawdopodobieństwo, że cholestorol powyżej 200, jezeli wystąpił zawał serca",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("Prawdopodbieństwo, że był wysoki poziom cukru jeżeli wystąpił zawał serca",p_sugar_if_heart_attack)