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[
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{
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"letter": "silk-1",
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"radical": "silk",
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"numeral": "1"
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},
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{
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"letter": "silk-2",
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"radical": "silk",
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"numeral": "2"
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},
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{
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"letter": "silk-3",
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"radical": "silk",
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"numeral": "3"
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},
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{
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"letter": "earth-1",
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"radical": "earth",
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"numeral": "4"
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}
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]
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# prototype to translate a system to text into dictionary
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# as a type of structured text
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import numpy as np
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import json
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# what is the proper way to store radicals with dictionary?
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# use json
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data = json.load(open('dict.json', 'r'))
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# the essence of the program is to introduce noise to disrupt
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# the mapping relation of the dictionary.
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# learning rules is also essential to machine learning, which
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for i in data:
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print(i["letter"])
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# modify numeral for letter and update json file
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i["numeral"] = "updated value nul"
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print(i["numeral"])
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for i in data:
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print(i)
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# how to save to json file
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# the problem is, dictionary keys are saved as strings
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# using numerical update can apply?
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# current rule
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# 1 <= key <= 3, silk; 4 <= key <= 6, earth; 7 <= key <= 9, water
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# add noise to rule
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# there is some noise
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noise = np.random.randint(1,3)
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# loop through keys, add noise to key value
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# say noise is 2, every key adds 2
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# dict = { '3':'silk-1', '4':'silk-2', '5':'silk-3',
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# '6':'earth-1', '7':'earth-2', '8':'earth-3',
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# '9':'water-1', '10':'water-2', '11':'water-3',
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# }
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# then when i query the dictionary again,
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# the rule is disrupted, the message is disrupted
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# the rule is disrupted by adding a simple number
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# are there more disruptive and complex rules?
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@ -0,0 +1,17 @@
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import numpy as np
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yong_grid = [[0,0,0,0,1,1,0,0,0,0],
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[0,0,1,1,1,1,0,0,1,0],
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[0,0,0,0,0,1,0,1,0,0],
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[0,0,0,0,0,1,1,0,0,0],
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[0,1,1,1,1,1,1,0,0,0],
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[0,0,0,1,0,1,1,0,0,0],
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[0,0,1,0,0,1,0,1,0,0],
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[0,1,0,0,0,1,0,0,1,0],
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[0,0,0,1,0,1,0,0,0,0],
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[0,0,0,0,1,1,0,0,0,0]]
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print(np.matrix(yong_grid))
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from matplotlib import pyplot as plt
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im = plt.imshow(yong_grid, cmap="copper_r")
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plt.show()
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