Main

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#------------------------------------------------------------
#	- 
# 	- Hello world, ML
# 	- 27-07-2020
# 	- aD
#-------------------------------------------------------------
!/usr/bin/env python3
from sklearn import tree 

#------------------------------------------------------------ # - # - Hello world, ML # - 27-07-2020 # - aD #------------------------------------------------------------- !/usr/bin/env python3 from sklearn import tree

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#test
print ("My first Neural Network\n")

#test print ("My first Neural Network\n")

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""" ======== SUPERVISED LEARNING STRUCTURE =============
|--------|			|-----------| 		|-----------|
|Collect |          |Train 	 	|       |Make 		|
|Training|    =>    |Classifier |	=>  |Predictions|
|	Data |          |			|       |			|
|--------|          |-----------|       |-----------|

"""

""" ======== SUPERVISED LEARNING STRUCTURE ============= |--------| |-----------| |-----------| |Collect | |Train | |Make | |Training| => |Classifier | => |Predictions| | Data | | | | | |--------| |-----------| |-----------| """

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""" Colleting input datas """
#Initial values
#features = [[140,"smooth"], [130,"smooth"], [150,"bumpy"], [170,"bumpy"]]
#labels = ["apple", "apple", "orange", "orange"]

""" Colleting input datas """ #Initial values #features = [[140,"smooth"], [130,"smooth"], [150,"bumpy"], [170,"bumpy"]] #labels = ["apple", "apple", "orange", "orange"]

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"""scikit-learn uses real-valued features """
#smooth : 1
#bumpy  : 0
features = [[140, 1], [130, 1], [150, 0], [170, 0]]
#apple : 0
#orange :1
labels = [0, 0, 1, 1]

"""scikit-learn uses real-valued features """ #smooth : 1 #bumpy : 0 features = [[140, 1], [130, 1], [150, 0], [170, 0]] #apple : 0 #orange :1 labels = [0, 0, 1, 1]

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""" Train Classifier (Box of RULES) """

""" Train Classifier (Box of RULES) """

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""" Decison Tree

	 weight > 150? 
		 / \
	yes /   \ No
Texture == bumby  ?
	 / \
yes /   \ No
 Orange apple
 
"""

""" Decison Tree weight > 150? / \ yes / \ No Texture == bumby ? / \ yes / \ No Orange apple """

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# creating a model
clf = tree.DecisionTreeClassifier()

# creating a model clf = tree.DecisionTreeClassifier()

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# training
#fit : find pattern in data
clf = clf.fit(features, labels) 

# training #fit : find pattern in data clf = clf.fit(features, labels)

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# prediction
print (clf.predict([[150, 0]]))

# prediction print (clf.predict([[150, 0]]))

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