Logistic Distribution

Author: heysushil

16.logistic_distribution.py

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+# Logistic Distribution
+
+'''
+IMP Points
+
+1. ye growth ko show karne ke liye use hota hai.
+2. ye bahot jaruri hai because aage chal ke ye ml aur neural networking me use hoga.
+3. ye continuous probability distribution hai
+4. isme 2 parameters hote hain pdf (probability density function) and cdf (cumulative density function)
+
+Logistic distribution arg: 
+    loc: mean , deafult me ye 0 (zero)
+    scale: standard deviation, default value is 1
+    size: matrix 
+'''
+
+from numpy import random as r
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+logistcVar = r.logistic(loc=1, scale=2, size=(2,3))
+print('\n\n',logistcVar,'\n')
+
+# use seaborn to get plto point
+# sns.distplot(r.logistic(loc=1, scale=1, size=(100)), hist=False)
+# use plt.show() to show graph
+sns.distplot(r.normal(loc=1, scale=2, size=(100)), hist=False, label='noraml')
+sns.distplot(r.logistic(loc=1, scale=1, size=(100)), hist=False, label='logstic')
+plt.show()
+
+
+'''
+Diff b/w Logistc and Normal Distribution
+
+1. logict jo hai ye normal ke comapristion me thoda log distance tak result deta hai.
+2. logistic and noraml both center pont result will be like same.
+'''

17.multinominal_distribution.py

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+# Multinomial Distribution
+
+'''
+IMP Points:
+
+1. ye binomial ka bhai hai wo kewal 2 hi condtion ke bich me probability find karta tha, jaise 0 ya 1 or head or tels.
+2. But Multinomial naam se hi pata chal raha hai ki 2 se jada case ke liye to jaise head aur tell to hoga but tie hone ke bhi chances hai tab kya hoga?
+'''
+
+from numpy import random as r
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+sns.distplot(r.multinomial(n=6, pvals=[1/6, 1/6, 1/6, 1/6, 1/6, 1/6]))
+print(r.multinomial(n=6, pvals=[1/6, 1/6, 1/6, 1/6, 1/6, 1/6]))
+plt.show()