TKG With Python

Difference between Type Stack (S) and Type Queue (Q) In this post, I explore the relationship between two learning types—Stack (S) and Queue (Q)—and their outcomes. Here, “Stack” and “Queue” are programming-inspired metaphors: S-type persons learn in a stack-like way, relying on memory, while Q-type persons learn in a queue-like way, relying on an internal image generator. In terms of abstraction, Learning Type (S or Q) is a higher-level concept, Integrated Learning Result is a mid-level concept, and Learning Result is a lower-level concept. For example, we might consider trust as an integrated learning result (a form of social capital), while money can be seen as a measurable outcome derived from that trust. In my code, the parameter b = 0.9 is introduced as an initial-condition bias, reflecting the influence of family inheritance and economic background. The horizontal axis is scaled so that x = 1.0 corresponds to 10 years of life, a scale derived from empirical observation...

 Learning Methods and Result Curves I sometimes think about learning methods, Stuck(S) and Que(Q). S tends to learn as y = 1.3X Q tends to learn as y = X**1.3 This difference shows short term winning for S, but if Q survives, Q wins for long term. import numpy as np import matplotlib.pyplot as plt # データ生成 x = np.linspace(0, 5, 100) y1 = 1.3 * x y2 = x ** 1.3 # グラフ描画 plt.figure(figsize=(8, 6)) plt.plot(x, y1, label=r"$y = 1.3x$") plt.plot(x, y2, label=r"$y = x^{1.3}$") plt.xlabel("x") plt.ylabel("y") plt.title("Comparison") plt.legend() plt.grid(True) plt.show()