update

double_11.py

@@ -1,19 +1,32 @@
+#
+# import numpy as np
+# import matplotlib.pyplot as plt
+#
+# years = [y+2009 for y in range(11)]
+# gmvs = [0.5, 9.36, 52, 191, 350, 571, 912, 1207, 1682, 2135, 2684]
+#
+#
+# def polynomial(xs, ys, n):
+#     f = np.polyfit(xs, ys, n)
+#     def func(x):
+#         return sum(f[i]*x**(n-i)for i in range(n+1))
+#     return func
+#
+# f = polynomial(years, gmvs, 3)
+# print(f(2020))
+#
+# plt.plot(years, gmvs, marker='o')
+# plt.show()
 
-import numpy as np
-import matplotlib.pyplot as plt
+if __name__ == '__main__':
+    daily_rate = 0.00009080
+    annual_rate = (1+daily_rate)**365
 
-years = [y+2009 for y in range(11)]
-gmvs = [0.5, 9.36, 52, 191, 350, 571, 912, 1207, 1682, 2135, 2684]
+    fv_list = [5, 5, 5, 105]
 
+    pv = 0
+    for i in range(len(fv_list)):
+        fv = fv_list[i]
+        pv += fv/annual_rate**(i+1)
 
-def polynomial(xs, ys, n):
-    f = np.polyfit(xs, ys, n)
-    def func(x):
-        return sum(f[i]*x**(n-i)for i in range(n+1))
-    return func
-
-f = polynomial(years, gmvs, 3)
-print(f(2020))
-
-plt.plot(years, gmvs, marker='o')
-plt.show()
+    print(pv)

nlp/gensim_demo/w2c.py

@@ -6,13 +6,21 @@
 
 if __name__ == '__main__':
 
+    # 包含了句子以及分词的处理
     # sentences = word2vec.Text8Corpus('text8')
+    # # sentences = list of list of words
     # model = Word2Vec(sentences, workers=cpu_count()//2)
     # model.save('text8.model')
+
     model = Word2Vec.load('text8.model')
+
+    # model.wv.vectors, model.wv.index2word
     # woman + king - man == ?
+    # woman + king - man == queen
     print(model.most_similar(positive=['woman', 'king'], negative=['man'], topn=2))
 
+
+
     woman_vec = model.wv.word_vec('woman', use_norm=True)
     king_vec = model.wv.word_vec('king', use_norm=True)
     man_vec = model.wv.word_vec('man', use_norm=True)

readme.md

@@ -1,33 +1,18 @@
 
 
-## 0. 计算机科学基础
 
-- algo/sort_and_search.py, [【Python番外】Python排序（TimSort，list.sort/sorted）与搜索（bisect_left/bisect_right/insort）
-](https://www.bilibili.com/video/av89285852)
-- algo/lexicographic_order.py, [【计算机基础算法】从排列到随机，字典序（lexicographic order）排列算法的实现](https://www.bilibili.com/video/BV1e7411y7cZ)
+### 合集列表
+
+- [深度学习调包侠](https://space.bilibili.com/59807853/channel/collectiondetail?sid=429351)
+- [经典神经网络模型拓扑结构（pytorch](https://space.bilibili.com/59807853/channel/collectiondetail?sid=446911)
+- [程序员不务正业系列](https://space.bilibili.com/59807853/channel/collectiondetail?sid=446276)
+- [深入理解概率统计](https://space.bilibili.com/59807853/channel/collectiondetail?sid=562732)
+- [面向cv的ffmpeg](https://space.bilibili.com/59807853/channel/collectiondetail?sid=545236)
+- [动手写 bert 系列](https://space.bilibili.com/59807853/channel/collectiondetail?sid=496538)
+- [深度学习面试系列](https://space.bilibili.com/59807853/channel/collectiondetail?sid=464585)
+- [手推公式](https://space.bilibili.com/59807853/channel/collectiondetail?sid=462509)
+- [python 机器学习](https://space.bilibili.com/59807853/channel/collectiondetail?sid=386935)
+- [csp：constraint satisfaction problem](https://space.bilibili.com/59807853/channel/collectiondetail?sid=413959)
+- [程序员说会计、经济、金融](https://space.bilibili.com/59807853/channel/collectiondetail?sid=615087)
 
 
-
-## 1. 趣味计算机科学
-
-- monte_carlo_triangle.py, [【计算机科学】蒙特卡洛方法计算“一棍砍两刀”构成三角形的概率](https://www.bilibili.com/video/av75100858/)
-- double_11.py, [【计算机科学】三阶多项式拟合，如何科学地预测2020年天猫双11交易额？](https://www.bilibili.com/video/av75833668/)
-
-## 2. 趣味数学
-
-
-- fun_math/e.py, [【基础数学】1.01^365/0.99^365有数量级的变化，为什么1.001^365/0.999^365却没有数量级的变化。它们跟自然常数e关系，复利与年化,科普,我的学习日记,数学,原创,科学,教育,科技,趣味科普人文,哔哩哔哩,Bilibili,B站,弹幕](https://www.bilibili.com/video/av79317053)
-
-
-## 3. 神经网络与计算机视觉
-
-- cv/image_similarity, [【计算机视觉】从图像距离（图像相似性）的计算（ahash/dhash/phash/whash）到以图搜索的实现（deep ranking）（一）](https://www.bilibili.com/video/av77627995)
-- cv/holiday_similarity, [【计算机视觉】基于 Siamese network 的图像相似性计算（keras 预训练网络及微调，多输入单输出）](https://www.bilibili.com/video/BV1cJ411v7vp)
-
-
-## 4. 统计
-
-- stats/, [【统计】统计检验（从t-distribution（t分布）到t-test（t检验），t-score（t-统计量）以及卡方检验（chi-test），excel计算](https://www.bilibili.com/video/av87641550)
-
-- stats/gini_index.py, [【统计】从MAD（mean absolute deviation）到Gini系数的计算（python实现）](https://www.bilibili.com/video/av92773925)
-