想不到我之前这么中二…

# Category Archives: Uncategorized

# Something interesting

不管哪个领域，都可以在上升期做科研，在平稳期做业务，在饱和期做教育，显然Andrew Ng是个明白人。

No matter what field you are in, you can do research in the growing period, dedicate into the industry in the steady period, and develop education in the saturation period. Obviously, Andrew Ng is a sensible person.

# 楽しい

按理说，我们二十五六岁，买车买房，工作体面，不能再抱怨了。但是房，车，体面，这些都是大路商品。我有，其他人可能有我的一百倍，一千倍还多。但是十年的青春时光，每个人都只有一次。

我希望看到这里的人，可以记得未来很重要，但是二十多岁一定要快意，可以努力奋斗也可以玩，可以富有也可以贫穷，但不能扭曲，要快意。

当你26岁决定自己要不要成为30岁的博士时，要记得这件事。

To be honest, we have a decent job, house, car at the age of 25, should not complain more. However, cars/houses/good jobs, all of them, are general commodities, others may have them of ten times or even of hundred times than of what I have. But the ten years of youth, everyone has only one time.

So, please remember this thing, when you are 26 years old and decide whether you want to be a 30-year-old Doctor.

# 机心

有机械者必有机事，有机事者必有机心。机心存于胸中，则纯白不备；纯白不备，则神生不定；神生不定者，道之所不载也。吾非不知，羞而不为也。

# Semiring

import numpy as np import networkx as nx from functools import reduce import matplotlib.pyplot as plt connect_graph = np.array([[0, 1, 0, 0, 0], [0, 0, 0, 1, 0], [0, 0, 0, 1, 0], [0, 0, 0, 0, 1], [0, 0, 1, 0, 0]]) def ring_add(a, b): return a or b def ring_multi(a, b): return a and b def dot_product(i, j): row = connect_graph[i] column = connect_graph[:,j] return reduce(ring_add, [ring_multi(a, b) for a, b in zip(row, column)]) def next_generation(connect_graph): candidate_number = connect_graph.shape[0] new_connect_graph = np.zeros((candidate_number, candidate_number)) for i in range(candidate_number): for j in range(candidate_number): new_connect_graph[i][j] = dot_product(i,j) return new_connect_graph new_connect_graph = next_generation(connect_graph) def draw_graph(connect_graph): G = nx.DiGraph() candidate_number = connect_graph.shape[0] node_name = list(range(candidate_number)) G.add_nodes_from(node_name) for i in range(candidate_number): for j in range(candidate_number): if connect_graph[i][j]: G.add_edge(i, j) nx.draw(G, with_labels=True) plt.show() draw_graph(new_connect_graph)

# 往明月多处走

# Variational inference for Bayes Network

In general neural networks have a sort of loss like that:

However, The part of the denominator integral is intractable of finding an analytic solution solution in practice. Therefore, we are going to make a distribution approaching the original distribution. KL divergence can be used to indicate the difference between these two distributions.

# 浓烟下的诗歌电台

We fall,

We break,

We fail,

But then,

We rise,

We heal,

We overcome.

如果有一天，你发现我在平庸面前低了头，请向我开炮。

# Increment One

__asm { moveax, dword ptr[i] inc eax mov dword ptr[i], eax }

lock inc dword ptr[i]

# Printing a pyramid matrix

How to print a pyramid matrix like that:

n = 2

[1, 1, 1]

[1, 2, 1]

[1, 1, 1]n = 3

[1, 1, 1, 1]

[1, 2, 2, 1]

[1, 2, 2, 1]

[1, 1, 1, 1]n = 4

[1, 1, 1, 1, 1]

[1, 2, 2, 2, 1]

[1, 1, 3, 2, 1]

[1, 2, 2, 2, 1]

[1, 1, 1, 1, 1]

def func(N): N += 1 matrix = [[1 for _ in range(N)] for _ in range(N)] cnt = 0 while cnt < N: # UP for i in range(cnt, N - cnt - 1): matrix[cnt][i] = cnt + 1 # RIGHT for i in range(cnt, N - cnt - 1): matrix[i][N - cnt - 1] = cnt + 1 # DOWN for i in range(N - cnt - 1, cnt, -1): matrix[N - cnt - 1][i] = cnt + 1 # LEFT for i in range(N - cnt, cnt, -1): matrix[N - cnt - 1][cnt] = cnt + 1 cnt += 1 return matrix if __name__ == "__main__": matrix = func(N=4) for line in matrix: print(line)