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Контрпример диагонального метода Кантора
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| numbers = [] | |
| k = 3 | |
| x = 0.4142135623730950488 # 2**0.5 - 1 | |
| for i in range(1, k): | |
| j = 10**i | |
| n = 1 / j | |
| c = x * j | |
| c = c - int(c) | |
| c = c / j | |
| a = 0 | |
| while True: | |
| b = round(c + round(a, i), 7) | |
| if b >= 1: | |
| break | |
| if b not in numbers: | |
| numbers.append(b) | |
| a += n | |
| for i in range(len(numbers)): | |
| print(i+1, ': ', numbers[i], '...', sep="") |
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| 1: 0.0142136... | |
| 2: 0.1142136... | |
| 3: 0.2142136... | |
| 4: 0.3142136... | |
| 5: 0.4142136... | |
| 6: 0.5142136... | |
| 7: 0.6142136... | |
| 8: 0.7142136... | |
| 9: 0.8142136... | |
| 10: 0.9142136... | |
| 11: 0.0042136... | |
| 12: 0.0242136... | |
| 13: 0.0342136... | |
| 14: 0.0442136... | |
| 15: 0.0542136... | |
| 16: 0.0642136... | |
| 17: 0.0742136... | |
| 18: 0.0842136... | |
| 19: 0.0942136... | |
| 20: 0.1042136... | |
| 21: 0.1242136... | |
| 22: 0.1342136... | |
| 23: 0.1442136... | |
| 24: 0.1542136... | |
| 25: 0.1642136... | |
| 26: 0.1742136... | |
| 27: 0.1842136... | |
| 28: 0.1942136... | |
| 29: 0.2042136... | |
| 30: 0.2242136... | |
| 31: 0.2342136... | |
| 32: 0.2442136... | |
| 33: 0.2542136... | |
| 34: 0.2642136... | |
| 35: 0.2742136... | |
| 36: 0.2842136... | |
| 37: 0.2942136... | |
| 38: 0.3042136... | |
| 39: 0.3242136... | |
| 40: 0.3342136... | |
| 41: 0.3442136... | |
| 42: 0.3542136... | |
| 43: 0.3642136... | |
| 44: 0.3742136... | |
| 45: 0.3842136... | |
| 46: 0.3942136... | |
| 47: 0.4042136... | |
| 48: 0.4242136... | |
| 49: 0.4342136... | |
| 50: 0.4442136... | |
| 51: 0.4542136... | |
| 52: 0.4642136... | |
| 53: 0.4742136... | |
| 54: 0.4842136... | |
| 55: 0.4942136... | |
| 56: 0.5042136... | |
| 57: 0.5242136... | |
| 58: 0.5342136... | |
| 59: 0.5442136... | |
| 60: 0.5542136... | |
| 61: 0.5642136... | |
| 62: 0.5742136... | |
| 63: 0.5842136... | |
| 64: 0.5942136... | |
| 65: 0.6042136... | |
| 66: 0.6242136... | |
| 67: 0.6342136... | |
| 68: 0.6442136... | |
| 69: 0.6542136... | |
| 70: 0.6642136... | |
| 71: 0.6742136... | |
| 72: 0.6842136... | |
| 73: 0.6942136... | |
| 74: 0.7042136... | |
| 75: 0.7242136... | |
| 76: 0.7342136... | |
| 77: 0.7442136... | |
| 78: 0.7542136... | |
| 79: 0.7642136... | |
| 80: 0.7742136... | |
| 81: 0.7842136... | |
| 82: 0.7942136... | |
| 83: 0.8042136... | |
| 84: 0.8242136... | |
| 85: 0.8342136... | |
| 86: 0.8442136... | |
| 87: 0.8542136... | |
| 88: 0.8642136... | |
| 89: 0.8742136... | |
| 90: 0.8842136... | |
| 91: 0.8942136... | |
| 92: 0.9042136... | |
| 93: 0.9242136... | |
| 94: 0.9342136... | |
| 95: 0.9442136... | |
| 96: 0.9542136... | |
| 97: 0.9642136... | |
| 98: 0.9742136... | |
| 99: 0.9842136... | |
| 100: 0.9942136... |
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