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seanybaby1122/README.md

prompt: # prompt: prompt: prompt: import networkx as nx

# import networkx as nx

# prompt: import matplotlib.pyplot as plt

# import matplotlib.pyplot as plt

# from matplotlib_venn import venn2

import matplotlib.pyplot as plt

# prompt: from matplotlib_venn import venn2

# import matplotlib.pyplot as plt from matplotlib_venn import venn2

# Assuming 'data' and 'graph' are defined from the previous code

# Assuming 'data' and 'graph' are defined from the previous code

# Step 3: Analyze and visualize the graph (example: Venn diagram)

known_words = set(data["words"].keys())

generated_words = set(node for node, attr in graph.nodes(data=True) if attr.get("category") == "Generated")

venn2([known_words, generated_words], ('Known Words', 'Generated Words'))

plt.title("Known vs. Generated Words")

plt.show()

# Step 3: Analyze and visualize the graph (example: Venn diagram)

known_words = set(data["words"].keys())

generated_words = set(node for node, attr in graph.nodes(data=True) if attr.get("category") == "Generated")

venn2([known_words, generated_words], ('Known Words', 'Generated Words'))

plt.title("Known vs. Generated Words")

plt.show()

# prompt: Define your symbolic data

# # data = {

# # "words": {

# # "DATA": {"category": "Information", "numeric": [68, 65, 84, 65]},

# # "AGRA": {"category": "Location", "numeric": [65, 71, 82, 65]},

# # },

# # "transformations": [

# # {"from": "DATA", "to": "ATAD", "type": "Reverse"},

# # {"from": "DATA", "to": "AGRA", "type": "Substitute"},

# # {"from": "AGRA", "to": "EMIT", "type": "Substitute"},

# # ],

# # }

data = {

"words": {

"DATA": {"category": "Information", "numeric": [68, 65, 84, 65]},

"AGRA": {"category": "Location", "numeric": [65, 71, 82, 65]},

},

"transformations": [

{"from": "DATA", "to": "ATAD", "type": "Reverse"},

{"from": "DATA", "to": "AGRA", "type": "Substitute"},

prompt: # prompt: prompt: import networkx as nx

# import networkx as nx

# prompt: import matplotlib.pyplot as plt

# import matplotlib.pyplot as plt

# from matplotlib_venn import venn2

import matplotlib.pyplot as plt

# prompt: from matplotlib_venn import venn2

# import matplotlib.pyplot as plt from matplotlib_venn import venn2

# Assuming 'data' and 'graph' are defined from the previous code

# Assuming 'data' and 'graph' are defined from the previous code

# Step 3: Analyze and visualize the graph (example: Venn diagram)

known_words = set(data["words"].keys())

generated_words = set(node for node, attr in graph.nodes(data=True) if attr.get("category") == "Generated")

venn2([known_words, generated_words], ('Known Words', 'Generated Words'))

plt.title("Known vs. Generated Words")

plt.show()

# Step 3: Analyze and visualize the graph (example: Venn diagram)

known_words = set(data["words"].keys())

generated_words = set(node for node, attr in graph.nodes(data=True) if attr.get("category") == "Generated")

venn2([known_words, generated_words], ('Known Words', 'Generated Words'))

plt.title("Known vs. Generated Words")

plt.show()

# prompt: Define your symbolic data

# # data = {

# # "words": {

# # "DATA": {"category": "Information", "numeric": [68, 65, 84, 65]},

# # "AGRA": {"category": "Location", "numeric": [65, 71, 82, 65]},

# # },

# # "transformations": [

# # {"from": "DATA", "to": "ATAD", "type": "Reverse"},

# # {"from": "DATA", "to": "AGRA", "type": "Substitute"},

# # {"from": "AGRA", "to": "EMIT", "type": "Substitute"},

# # ],

# # }

data = {

"words": {

"DATA": {"category": "Information", "numeric": [68, 65, 84, 65]},

"AGRA": {"category": "Location", "numeric": [65, 71, 82, 65]},

},

"transformations": [

{"from": "DATA", "to": "ATAD", "type": "Reverse"},

{"from": "DATA", "to": "AGRA", "type": "Substitute"},

{"from": "AGRA", "to": "EMIT", "type": "Substitute"},

],

}

.github/workflows/docker-ci.yml

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