From 918fa6c94b9a70fdc7810f52850b07dca9f3d8d0 Mon Sep 17 00:00:00 2001
From: Fan Jiang <ProfFan@users.noreply.github.com>
Date: Fri, 6 Nov 2020 23:38:08 -0500
Subject: [PATCH 1/4] Create writeup.md

---
 players/ProfFan/writeup.md | 9 +++++++++
 1 file changed, 9 insertions(+)
 create mode 100644 players/ProfFan/writeup.md

diff --git a/players/ProfFan/writeup.md b/players/ProfFan/writeup.md
new file mode 100644
index 0000000..a5f2e03
--- /dev/null
+++ b/players/ProfFan/writeup.md
@@ -0,0 +1,9 @@
+今年实在太忙，只做了一题
+
+# 证验码
+
+注意到题目给的是 BMP 格式，显然是为了保存图片的一些统计特征。继续审代码，发现随机数均为真随机，故这个方向没有价值。众所周知字体渲染是有 Anti-aliasing 的，所以每个字都有独特的灰度分布。
+故我们可以计算每张图片的灰度分布，再加上预计算每个单独字符的灰度分布，得到直方图，在直方图上做优化即可得到验证码。
+
+注意此处应当使用 L1 而不是 L2 优化，因为16个字符远少于 26*2+10，结果应当是稀疏的。本题用 L2 也可以得到解，但是如果增加干扰线条的密度，L2 应该会失败。
+

From 4a6e03e0460a26fd400560340de30c946b98cd53 Mon Sep 17 00:00:00 2001
From: Fan Jiang <i@fanjiang.me>
Date: Fri, 6 Nov 2020 23:41:45 -0500
Subject: [PATCH 2/4] Add code

---
 players/ProfFan/solver_files/solver_5_1.png |  Bin 0 -> 65598 bytes
 players/ProfFan/solver_files/solver_5_1.svg |  562 ++
 players/ProfFan/solver_files/solver_6_0.png |  Bin 0 -> 56616 bytes
 players/ProfFan/solver_files/solver_6_0.svg | 7442 +++++++++++++++++++
 players/ProfFan/writeup.md                  |  469 +-
 5 files changed, 8472 insertions(+), 1 deletion(-)
 create mode 100644 players/ProfFan/solver_files/solver_5_1.png
 create mode 100644 players/ProfFan/solver_files/solver_5_1.svg
 create mode 100644 players/ProfFan/solver_files/solver_6_0.png
 create mode 100644 players/ProfFan/solver_files/solver_6_0.svg

diff --git a/players/ProfFan/solver_files/solver_5_1.png b/players/ProfFan/solver_files/solver_5_1.png
new file mode 100644
index 0000000000000000000000000000000000000000..9b291e28e421bff32789b9ee013771e186ed3e0b
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new file mode 100644
index 0000000..186a459
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new file mode 100644
index 0000000..f594ee7
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diff --git a/players/ProfFan/writeup.md b/players/ProfFan/writeup.md
index a5f2e03..becff46 100644
--- a/players/ProfFan/writeup.md
+++ b/players/ProfFan/writeup.md
@@ -1,4 +1,4 @@
-今年实在太忙，只做了一题
+今年实在太忙，只做了一题orz
 
 # 证验码
 
@@ -7,3 +7,470 @@
 
 注意此处应当使用 L1 而不是 L2 优化，因为16个字符远少于 26*2+10，结果应当是稀疏的。本题用 L2 也可以得到解，但是如果增加干扰线条的密度，L2 应该会失败。
 
+# 代码
+
+```python
+%matplotlib inline
+
+import matplotlib.pyplot as plt
+import numpy as np
+import random, string
+```
+
+
+```python
+import shuffle
+```
+
+
+```python
+# !wget https://github.com/adobe-fonts/source-code-pro/raw/release/TTF/SourceCodePro-Light.ttf
+```
+
+
+```python
+alphabet = sorted(string.digits + string.ascii_letters)
+#code = "".join([random.choice(alphabet) for _ in range(16)])
+code = "01234567890ABcde"
+print("Code:", code)
+```
+
+    Code: 01234567890ABcde
+
+
+
+```python
+original = shuffle.generate_captcha(code, shuffle_mode=False)
+shuffled = shuffle.generate_captcha(code, shuffle_mode=True)
+```
+
+
+```python
+plt.subplot(211)
+plt.imshow(original)
+plt.subplot(212)
+plt.imshow(shuffled)
+```
+
+
+
+
+    <matplotlib.image.AxesImage at 0x7f8312f56970>
+
+
+
+
+![svg](solver_files/solver_5_1.svg)
+
+
+
+```python
+fig, axs = plt.subplots(2, 10, figsize=(20,6))
+for i in range(10):
+    test_onealpha = np.array(shuffle.img_generate("{}".format(i)))
+    axs[0][i].imshow(test_onealpha)
+    counts = np.bincount(test_onealpha[:, :, 0].flatten(),minlength=255)
+    axs[1][i].plot(counts)
+    axs[1][i].set_yscale('log')
+```
+
+
+![svg](solver_files/solver_6_0.svg)
+
+
+
+```python
+counts_full = []
+for k in range(3):
+    counts = np.bincount(np.array(shuffled)[:, :, 0].flatten(),minlength=256)
+    counts[255] = 0
+    counts_full.append(counts)
+counts_full = np.hstack(counts_full)
+print(counts_full.shape)
+```
+
+    (768,)
+
+
+
+```python
+# %pip install cvxpy
+```
+
+
+```python
+import cvxpy as cvx
+```
+
+
+```python
+bases = []
+
+for i in range(len(alphabet)):
+    bases_rgb = []
+    for k in range(3):
+        test_onealpha = np.array(shuffle.img_generate("{}".format(alphabet[i])))
+        counts = np.bincount(test_onealpha[:, :, k].flatten(),minlength=256)
+        counts[255] = 0
+        bases_rgb.append(counts)
+    bases.append(np.hstack(bases_rgb))
+
+A = np.vstack(bases).T
+print(A.shape)
+print(A.T)
+```
+
+    (768, 62)
+    [[224   5   2 ...   3   5   0]
+     [134   1   1 ...   0   2   0]
+     [171   2   1 ...   3   4   0]
+     ...
+     [ 94   1   2 ...   2   6   0]
+     [118   4   3 ...   2   6   0]
+     [195   2   2 ...   1   3   0]]
+    /usr/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
+      and should_run_async(code)
+
+
+
+```python
+import PIL
+shuffled = PIL.Image.open("./captcha_shuffled.bmp")
+# Solve actual problem
+counts_full = []
+for k in range(3):
+    counts = np.bincount(np.array(shuffled)[:, :, 0].flatten(),minlength=256)
+    counts[255] = 0
+    counts_full.append(counts)
+counts_full = np.hstack(counts_full)
+print(counts_full.shape)
+```
+
+    (768,)
+    /usr/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
+      and should_run_async(code)
+
+
+
+```python
+vx = cvx.Variable(len(alphabet))
+objective = cvx.Minimize(cvx.norm(A*vx - counts_full, 1))
+constraints = [vx >= 0]
+prob = cvx.Problem(objective, constraints)
+result = prob.solve(verbose=True)
+```
+
+    
+    ECOS 2.0.7 - (C) embotech GmbH, Zurich Switzerland, 2012-15. Web: www.embotech.com/ECOS
+    
+    It     pcost       dcost      gap   pres   dres    k/t    mu     step   sigma     IR    |   BT
+     0  -2.788e-13  +1.258e-12  +5e+05  8e-01  4e-01  1e+00  3e+02    ---    ---    1  2  - |  -  - 
+     1  +4.640e+03  +4.675e+03  +2e+05  3e-01  1e-01  3e+01  1e+02  0.7401  2e-01   1  1  1 |  0  0
+     2  +5.846e+03  +5.861e+03  +9e+04  1e-01  4e-02  1e+01  6e+01  0.6585  8e-02   1  1  1 |  0  0
+     3  +5.968e+03  +5.970e+03  +1e+04  1e-02  3e-03  2e+00  6e+00  0.9494  6e-02   1  1  1 |  0  0
+     4  +6.080e+03  +6.080e+03  +3e+03  4e-03  1e-03  5e-01  2e+00  0.7566  6e-02   1  1  1 |  0  0
+     5  +6.151e+03  +6.152e+03  +2e+03  2e-03  6e-04  3e-01  1e+00  0.6536  5e-01   1  1  1 |  0  0
+     6  +6.193e+03  +6.193e+03  +9e+02  1e-03  3e-04  1e-01  5e-01  0.6015  1e-01   1  1  1 |  0  0
+     7  +6.198e+03  +6.198e+03  +8e+02  1e-03  3e-04  1e-01  5e-01  0.2129  7e-01   1  1  1 |  0  0
+     8  +6.216e+03  +6.216e+03  +6e+02  9e-04  2e-04  1e-01  3e-01  0.5360  4e-01   1  1  1 |  0  0
+     9  +6.230e+03  +6.230e+03  +3e+02  6e-04  1e-04  6e-02  2e-01  0.6585  4e-01   1  1  1 |  0  0
+    10  +6.244e+03  +6.244e+03  +2e+02  3e-04  5e-05  3e-02  1e-01  0.6652  2e-01   1  1  1 |  0  0
+    11  +6.249e+03  +6.249e+03  +1e+02  2e-04  3e-05  2e-02  7e-02  0.5025  4e-01   1  1  1 |  0  0
+    12  +6.255e+03  +6.255e+03  +6e+01  1e-04  2e-05  1e-02  4e-02  0.7639  3e-01   1  1  1 |  0  0
+    13  +6.259e+03  +6.259e+03  +3e+01  5e-05  7e-06  6e-03  2e-02  0.6280  1e-01   1  1  1 |  0  0
+    14  +6.261e+03  +6.261e+03  +1e+01  2e-05  3e-06  2e-03  7e-03  0.7859  2e-01   1  1  1 |  0  0
+    15  +6.262e+03  +6.262e+03  +4e+00  8e-06  1e-06  9e-04  2e-03  0.7343  1e-01   1  1  1 |  0  0
+    16  +6.263e+03  +6.263e+03  +8e-01  2e-06  2e-07  2e-04  5e-04  0.8474  6e-02   1  1  1 |  0  0
+    17  +6.263e+03  +6.263e+03  +3e-01  7e-07  9e-08  8e-05  2e-04  0.6258  6e-02   1  1  1 |  0  0
+    18  +6.263e+03  +6.263e+03  +4e-02  7e-08  1e-08  8e-06  2e-05  0.9127  2e-02   1  1  1 |  0  0
+    19  +6.263e+03  +6.263e+03  +2e-03  3e-09  4e-10  4e-07  1e-06  0.9579  2e-04   2  1  1 |  0  0
+    20  +6.263e+03  +6.263e+03  +2e-05  3e-11  5e-12  4e-09  1e-08  0.9890  1e-04   2  1  1 |  0  0
+    
+    OPTIMAL (within feastol=3.4e-11, reltol=2.7e-09, abstol=1.7e-05).
+    Runtime: 0.048064 seconds.
+    
+    /home/fan/.local/lib/python3.8/site-packages/cvxpy/expressions/expression.py:550: UserWarning: 
+    This use of ``*`` has resulted in matrix multiplication.
+    Using ``*`` for matrix multiplication has been deprecated since CVXPY 1.1.
+        Use ``*`` for matrix-scalar and vector-scalar multiplication.
+        Use ``@`` for matrix-matrix and matrix-vector multiplication.
+        Use ``multiply`` for elementwise multiplication.
+    
+      warnings.warn(__STAR_MATMUL_WARNING__, UserWarning)
+
+
+
+```python
+print(*zip(alphabet, vx.value.round(0)))
+```
+
+    ('0', 1.0) ('1', 0.0) ('2', -0.0) ('3', 0.0) ('4', -0.0) ('5', 1.0) ('6', 0.0) ('7', -0.0) ('8', 0.0) ('9', -0.0) ('A', 0.0) ('B', -0.0) ('C', -0.0) ('D', -0.0) ('E', 1.0) ('F', 0.0) ('G', -0.0) ('H', 1.0) ('I', -0.0) ('J', -0.0) ('K', 0.0) ('L', 1.0) ('M', -0.0) ('N', -0.0) ('O', 0.0) ('P', 1.0) ('Q', -0.0) ('R', -0.0) ('S', 1.0) ('T', -0.0) ('U', 0.0) ('V', 0.0) ('W', 1.0) ('X', 1.0) ('Y', 0.0) ('Z', 1.0) ('a', -0.0) ('b', 0.0) ('c', 0.0) ('d', 0.0) ('e', -0.0) ('f', -0.0) ('g', -0.0) ('h', -0.0) ('i', -0.0) ('j', -0.0) ('k', -0.0) ('l', 2.0) ('m', -0.0) ('n', -0.0) ('o', -0.0) ('p', -0.0) ('q', 0.0) ('r', -0.0) ('s', 1.0) ('t', -0.0) ('u', 1.0) ('v', 0.0) ('w', 1.0) ('x', 1.0) ('y', -0.0) ('z', -0.0)
+
+
+
+```python
+print(A @ np.array([[2,2,2,2,2,2, 1,1,1,1]]).T - np.expand_dims(counts_full,axis=1))
+```
+
+    [[ 159]
+     [   2]
+     [   2]
+     [   0]
+     [-109]
+     [   0]
+     [   4]
+     [   3]
+     [   2]
+     [   1]
+     [   4]
+     [   5]
+     [   1]
+     [   0]
+     [   0]
+     [   1]
+     [   4]
+     [   3]
+     [   0]
+     [   1]
+     [   0]
+     [   1]
+     [   2]
+     [   1]
+     [   1]
+     [   1]
+     [   3]
+     [   3]
+     [   0]
+     [   0]
+     [   1]
+     [   1]
+     [   0]
+     [   2]
+     [   0]
+     [   5]
+     [   1]
+     [   2]
+     [   1]
+     [   1]
+     [   1]
+     [   1]
+     [   0]
+     [   1]
+     [   0]
+     [   0]
+     [   2]
+     [   0]
+     [   0]
+     [   1]
+     [   1]
+     [   2]
+     [   1]
+     [   2]
+     [   0]
+     [   1]
+     [   0]
+     [   0]
+     [   0]
+     [   0]
+     [   0]
+     [   0]
+     [   1]
+     [   1]
+     [   0]
+     [   1]
+     [   1]
+     [   0]
+     [   0]
+     [   1]
+     [   2]
+     [   2]
+     [   2]
+     [   1]
+     [   0]
+     [   0]
+     [   0]
+     [   0]
+     [   0]
+     [   0]
+     [   0]
+     [   1]
+     [   0]
+     [   0]
+     [   0]
+     [   0]
+     [   0]
+     [   0]
+     [   0]
+     [   1]
+     [   1]
+     [   0]
+     [   0]
+     [   0]
+     [   1]
+     [   2]
+     [   1]
+     [   0]
+     [   0]
+     [   0]
+     [   1]
+     [   1]
+     [   0]
+     [   0]
+     [   0]
+     [   0]
+     [   0]
+     [   0]
+     [   1]
+     [   0]
+     [   0]
+     [   0]
+     [   0]
+     [   1]
+     [   0]
+     [   1]
+     [   1]
+     [   0]
+     [   0]
+     [-238]
+     [   0]
+     [   1]
+     [   0]
+     [   0]
+     [   0]
+     [   0]
+     [   0]
+     [   0]
+     [   0]
+     [   0]
+     [   0]
+     [   0]
+     [   1]
+     [   0]
+     [   0]
+     [   0]
+     [   0]
+     [   0]
+     [   1]
+     [   0]
+     [   0]
+     [   1]
+     [   0]
+     [   1]
+     [-147]
+     [   0]
+     [   0]
+     [   1]
+     [-204]
+     [   1]
+     [   2]
+     [   1]
+     [   0]
+     [   2]
+     [   1]
+     [   0]
+     [   0]
+     [   0]
+     [   0]
+     [   1]
+     [   2]
+     [   2]
+     [   1]
+     [   1]
+     [   1]
+     [   0]
+     [   1]
+     [   0]
+     [   1]
+     [   1]
+     [   2]
+     [   4]
+     [-149]
+     [   0]
+     [   2]
+     [   0]
+     [   1]
+     [   0]
+     [   1]
+     [   0]
+     [   1]
+     [   0]
+     [   1]
+     [   0]
+     [   0]
+     [   0]
+     [   1]
+     [   1]
+     [   0]
+     [   1]
+     [   0]
+     [   2]
+     [   2]
+     [   0]
+     [   0]
+     [   0]
+     [   0]
+     [   0]
+     [   1]
+     [   2]
+     [   1]
+     [   0]
+     [   1]
+     [   2]
+     [   0]
+     [   0]
+     [-323]
+     [-154]
+     [   1]
+     [   0]
+     [   0]
+     [   0]
+     [   1]
+     [   2]
+     [   0]
+     [   1]
+     [   0]
+     [   0]
+     [   0]
+     [-338]
+     [   1]
+     [   0]
+     [   0]
+     [   2]
+     [   0]
+     [   0]
+     [   2]
+     [   1]
+     [   2]
+     [   1]
+     [   2]
+     [   2]
+     [   1]
+     [   2]
+     [   1]
+     [   0]
+     [-215]
+     [   0]
+     [   0]
+     [   0]
+     [   0]
+     [   0]
+     [   2]
+     [   2]
+     [   1]
+     [ -94]
+     [   1]
+     [   0]
+     [   1]
+     [   1]
+     [   0]
+     [   2]
+     [   1]
+     [   0]
+     [   4]
+     [   0]]
+
+
+
+```python
+
+```

From e9ef12bd74fca91dc296888136bb5386c023c9d1 Mon Sep 17 00:00:00 2001
From: Fan Jiang <i@fanjiang.me>
Date: Fri, 6 Nov 2020 23:43:03 -0500
Subject: [PATCH 3/4] Remove unnecessary files

---
 players/ProfFan/writeup.md | 268 -------------------------------------
 1 file changed, 268 deletions(-)

diff --git a/players/ProfFan/writeup.md b/players/ProfFan/writeup.md
index becff46..ee13dbb 100644
--- a/players/ProfFan/writeup.md
+++ b/players/ProfFan/writeup.md
@@ -206,271 +206,3 @@ print(*zip(alphabet, vx.value.round(0)))
 
     ('0', 1.0) ('1', 0.0) ('2', -0.0) ('3', 0.0) ('4', -0.0) ('5', 1.0) ('6', 0.0) ('7', -0.0) ('8', 0.0) ('9', -0.0) ('A', 0.0) ('B', -0.0) ('C', -0.0) ('D', -0.0) ('E', 1.0) ('F', 0.0) ('G', -0.0) ('H', 1.0) ('I', -0.0) ('J', -0.0) ('K', 0.0) ('L', 1.0) ('M', -0.0) ('N', -0.0) ('O', 0.0) ('P', 1.0) ('Q', -0.0) ('R', -0.0) ('S', 1.0) ('T', -0.0) ('U', 0.0) ('V', 0.0) ('W', 1.0) ('X', 1.0) ('Y', 0.0) ('Z', 1.0) ('a', -0.0) ('b', 0.0) ('c', 0.0) ('d', 0.0) ('e', -0.0) ('f', -0.0) ('g', -0.0) ('h', -0.0) ('i', -0.0) ('j', -0.0) ('k', -0.0) ('l', 2.0) ('m', -0.0) ('n', -0.0) ('o', -0.0) ('p', -0.0) ('q', 0.0) ('r', -0.0) ('s', 1.0) ('t', -0.0) ('u', 1.0) ('v', 0.0) ('w', 1.0) ('x', 1.0) ('y', -0.0) ('z', -0.0)
 
-
-
-```python
-print(A @ np.array([[2,2,2,2,2,2, 1,1,1,1]]).T - np.expand_dims(counts_full,axis=1))
-```
-
-    [[ 159]
-     [   2]
-     [   2]
-     [   0]
-     [-109]
-     [   0]
-     [   4]
-     [   3]
-     [   2]
-     [   1]
-     [   4]
-     [   5]
-     [   1]
-     [   0]
-     [   0]
-     [   1]
-     [   4]
-     [   3]
-     [   0]
-     [   1]
-     [   0]
-     [   1]
-     [   2]
-     [   1]
-     [   1]
-     [   1]
-     [   3]
-     [   3]
-     [   0]
-     [   0]
-     [   1]
-     [   1]
-     [   0]
-     [   2]
-     [   0]
-     [   5]
-     [   1]
-     [   2]
-     [   1]
-     [   1]
-     [   1]
-     [   1]
-     [   0]
-     [   1]
-     [   0]
-     [   0]
-     [   2]
-     [   0]
-     [   0]
-     [   1]
-     [   1]
-     [   2]
-     [   1]
-     [   2]
-     [   0]
-     [   1]
-     [   0]
-     [   0]
-     [   0]
-     [   0]
-     [   0]
-     [   0]
-     [   1]
-     [   1]
-     [   0]
-     [   1]
-     [   1]
-     [   0]
-     [   0]
-     [   1]
-     [   2]
-     [   2]
-     [   2]
-     [   1]
-     [   0]
-     [   0]
-     [   0]
-     [   0]
-     [   0]
-     [   0]
-     [   0]
-     [   1]
-     [   0]
-     [   0]
-     [   0]
-     [   0]
-     [   0]
-     [   0]
-     [   0]
-     [   1]
-     [   1]
-     [   0]
-     [   0]
-     [   0]
-     [   1]
-     [   2]
-     [   1]
-     [   0]
-     [   0]
-     [   0]
-     [   1]
-     [   1]
-     [   0]
-     [   0]
-     [   0]
-     [   0]
-     [   0]
-     [   0]
-     [   1]
-     [   0]
-     [   0]
-     [   0]
-     [   0]
-     [   1]
-     [   0]
-     [   1]
-     [   1]
-     [   0]
-     [   0]
-     [-238]
-     [   0]
-     [   1]
-     [   0]
-     [   0]
-     [   0]
-     [   0]
-     [   0]
-     [   0]
-     [   0]
-     [   0]
-     [   0]
-     [   0]
-     [   1]
-     [   0]
-     [   0]
-     [   0]
-     [   0]
-     [   0]
-     [   1]
-     [   0]
-     [   0]
-     [   1]
-     [   0]
-     [   1]
-     [-147]
-     [   0]
-     [   0]
-     [   1]
-     [-204]
-     [   1]
-     [   2]
-     [   1]
-     [   0]
-     [   2]
-     [   1]
-     [   0]
-     [   0]
-     [   0]
-     [   0]
-     [   1]
-     [   2]
-     [   2]
-     [   1]
-     [   1]
-     [   1]
-     [   0]
-     [   1]
-     [   0]
-     [   1]
-     [   1]
-     [   2]
-     [   4]
-     [-149]
-     [   0]
-     [   2]
-     [   0]
-     [   1]
-     [   0]
-     [   1]
-     [   0]
-     [   1]
-     [   0]
-     [   1]
-     [   0]
-     [   0]
-     [   0]
-     [   1]
-     [   1]
-     [   0]
-     [   1]
-     [   0]
-     [   2]
-     [   2]
-     [   0]
-     [   0]
-     [   0]
-     [   0]
-     [   0]
-     [   1]
-     [   2]
-     [   1]
-     [   0]
-     [   1]
-     [   2]
-     [   0]
-     [   0]
-     [-323]
-     [-154]
-     [   1]
-     [   0]
-     [   0]
-     [   0]
-     [   1]
-     [   2]
-     [   0]
-     [   1]
-     [   0]
-     [   0]
-     [   0]
-     [-338]
-     [   1]
-     [   0]
-     [   0]
-     [   2]
-     [   0]
-     [   0]
-     [   2]
-     [   1]
-     [   2]
-     [   1]
-     [   2]
-     [   2]
-     [   1]
-     [   2]
-     [   1]
-     [   0]
-     [-215]
-     [   0]
-     [   0]
-     [   0]
-     [   0]
-     [   0]
-     [   2]
-     [   2]
-     [   1]
-     [ -94]
-     [   1]
-     [   0]
-     [   1]
-     [   1]
-     [   0]
-     [   2]
-     [   1]
-     [   0]
-     [   4]
-     [   0]]
-
-
-
-```python
-
-```

From a6445a765e9613d19471319b76d385e69af2ad1f Mon Sep 17 00:00:00 2001
From: Fan Jiang <i@fanjiang.me>
Date: Fri, 6 Nov 2020 23:45:31 -0500
Subject: [PATCH 4/4] update comments

---
 players/ProfFan/writeup.md | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/players/ProfFan/writeup.md b/players/ProfFan/writeup.md
index ee13dbb..b5eb195 100644
--- a/players/ProfFan/writeup.md
+++ b/players/ProfFan/writeup.md
@@ -1,11 +1,13 @@
-今年实在太忙，只做了一题orz
+今年肝 paper 实在太忙，只做了一题和自己研究相关的（
+
+顺便膜各位师傅 orz
 
 # 证验码
 
 注意到题目给的是 BMP 格式，显然是为了保存图片的一些统计特征。继续审代码，发现随机数均为真随机，故这个方向没有价值。众所周知字体渲染是有 Anti-aliasing 的，所以每个字都有独特的灰度分布。
 故我们可以计算每张图片的灰度分布，再加上预计算每个单独字符的灰度分布，得到直方图，在直方图上做优化即可得到验证码。
 
-注意此处应当使用 L1 而不是 L2 优化，因为16个字符远少于 26*2+10，结果应当是稀疏的。本题用 L2 也可以得到解，但是如果增加干扰线条的密度，L2 应该会失败。
+注意此处应当使用 L1 而不是 L2 (最小二乘）优化，因为16个字符远少于 26*2+10，结果应当是稀疏的。本题用 L2 也可以得到解，但是如果增加干扰线条的密度，L2 应该会失败。
 
 # 代码