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cosanlab · Jan 1, 2024 · 6221a47 · 6221a47
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diff --git a/_images/e86d6caf36a3d61015095ef2e930b5a6c4fd8307911f620e7c3a6b0207d1dccf.png b/_images/e86d6caf36a3d61015095ef2e930b5a6c4fd8307911f620e7c3a6b0207d1dccf.png
diff --git a/notebooks/cryptography.html b/notebooks/cryptography.html
@@ -579,22 +579,22 @@ <h2>Run the cell below to download data and prepare our environment!<a class="he
         white-space: pre-wrap;
     }
   </style>
-  </div><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Vqowuyp 1
+  </div><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Znxqejy 1
 
 
-Rmp Fosuyp Yssmtu, ta Nyssedvq Qoss, md Rtcypryurqmpy, for o cod fqt,
-atp qmr tfd ocxrycydu, dykyp uttn xw ode bttn bxu uqy Boptdyuoiy;
-uqypy qy atxdh tvvxwoumtd atp od mhsy qtxp, odh vtdrtsoumtd md o
-hmrupyrryh tdy; uqypy qmr aovxsumyr fypy ptxryh mdut ohcmpoumtd odh
-pyrwyvu, be vtduycwsoumdi uqy smcmuyh pycdodu ta uqy yopsmyru wouydur;
-uqypy ode xdfysvtcy rydroumtdr, opmrmdi aptc htcyrumv oaaompr
-vqodiyh douxposse mdut wmue odh vtduycwu or qy uxpdyh tkyp
-uqy osctru ydhsyrr vpyoumtdr ta uqy soru vyduxpe; odh uqypy,
-ma ykype tuqyp syoa fypy wtfypsyrr, qy vtxsh pyoh qmr tfd qmrutpe
-fmuq od mduypyru fqmvq dykyp aomsyh.  Uqmr for uqy woiy ou fqmvq
-uqy aoktxpmuy ktsxcy osfoer twydyh:
+Dgy Sxwejy Jwwgke, ki Ljwwatzn Nxww, gt Dkvjydjedngyj, sxd x vxt snk,
+iky ngd kst xvhdjvjte, tjbjy ekkl hq xta rkkl rhe enj Rxyktjexmj;
+enjyj nj ikhtc kzzhqxegkt iky xt gcwj nkhy, xtc zktdkwxegkt gt x
+cgdeyjddjc ktj; enjyj ngd ixzhwegjd sjyj ykhdjc gtek xcvgyxegkt xtc
+yjdqjze, ra zktejvqwxegtm enj wgvgejc yjvtxte ki enj jxywgjde qxejted;
+enjyj xta htsjwzkvj djtdxegktd, xygdgtm iykv ckvjdegz xiixgyd
+znxtmjc txehyxwwa gtek qgea xtc zktejvqe xd nj ehytjc kbjy
+enj xwvkde jtcwjdd zyjxegktd ki enj wxde zjtehya; xtc enjyj,
+gi jbjya kenjy wjxi sjyj qksjywjdd, nj zkhwc yjxc ngd kst ngdekya
+sgen xt gtejyjde sngzn tjbjy ixgwjc.  Engd sxd enj qxmj xe sngzn
+enj ixbkhygej bkwhvj xwsxad kqjtjc:
 
-           &quot;YSSMTU TA NYSSEDVQ QOSS.
+           &quot;JWWGKE KI LJWWATZN NXWW.
 
 &quot;
 </pre></div>
@@ -661,8 +661,8 @@ <h2>Run the cell below to download data and prepare our environment!<a class="he
         white-space: pre-wrap;
     }
   </style>
-  </div><img alt="../_images/584d0457145418043b972f331d98072bc406dd928ebd9726f70e8ddca44a0f68.png" src="../_images/584d0457145418043b972f331d98072bc406dd928ebd9726f70e8ddca44a0f68.png" />
-<img alt="../_images/787c1f4a787bf448d3e817a91e6e613d55283bc2419a5660a232d11101a9f939.png" src="../_images/787c1f4a787bf448d3e817a91e6e613d55283bc2419a5660a232d11101a9f939.png" />
+  </div><img alt="../_images/83fa9d4bfa53ec406c65e98357e952724817ced2f5fddd8f72b8d88ca21588d6.png" src="../_images/83fa9d4bfa53ec406c65e98357e952724817ced2f5fddd8f72b8d88ca21588d6.png" />
+<img alt="../_images/84697fb5df5428ad73bdba0e0298ada6f641170fd23e38d28177ed65ec293f86.png" src="../_images/84697fb5df5428ad73bdba0e0298ada6f641170fd23e38d28177ed65ec293f86.png" />
 </div>
 </div>
 <p>As the above distribution shows us, even though each individual letter in the encrypted text no longer has the same occurance probability as it does in typical English text, the general shape of the re-ordered distribution is still the same. We can use this to build a first-pass remapping to attempt deciphering the encrypted text, substituting each encrypted letter with the English letter closest in usage frequency.</p>
@@ -823,7 +823,7 @@ <h2>Run the cell below to download data and prepare our environment!<a class="he
         white-space: pre-wrap;
     }
   </style>
-  </div><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>swap Q and D 	percent valid English words: 0.5160721105097512 
+  </div><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>swap N and T 	percent valid English words: 0.5160721105097512 
 
 
 Fhabter 1
@@ -846,7 +846,7 @@ <h2>Run the cell below to download data and prepare our environment!<a class="he
 &quot;
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>swap H and S 	percent valid English words: 0.6301505155453719 
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>swap C and W 	percent valid English words: 0.6301505155453719 
 
 
 Fhabter 1
@@ -869,7 +869,7 @@ <h2>Run the cell below to download data and prepare our environment!<a class="he
 &quot;
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>swap B and W 	percent valid English words: 0.6855502442750102 
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>swap R and Q 	percent valid English words: 0.6855502442750102 
 
 
 Fhapter 1
@@ -892,7 +892,7 @@ <h2>Run the cell below to download data and prepare our environment!<a class="he
 &quot;
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>swap V and A 	percent valid English words: 0.8056729743610003 
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>swap Z and I 	percent valid English words: 0.8056729743610003 
 
 
 Chapter 1
@@ -915,7 +915,30 @@ <h2>Run the cell below to download data and prepare our environment!<a class="he
 &quot;
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>swap L and G 	percent valid English words: 0.8100317359986304 
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>swap F and P 	percent valid English words: 0.8099658936778203 
+
+
+Chapter 1
+
+
+Sir Walter Elliot, of Kellynch Hall, in Somersetshire, was a man who,
+for his own amusement, never took up any book but the Baronetage;
+there he found occupation for an idle hour, and consolation in a
+distressed one; there his faculties were roused into admiration and
+respect, by contemplating the limited remnant of the earliest patents;
+there any unwelcome sensations, arising from domestic affairs
+changed naturally into pity and contempt as he turned over
+the almost endless creations of the last century; and there,
+if every other leaf were powerless, he could read his own history
+with an interest which never failed.  This was the page at which
+the favourite volume always opened:
+
+           &quot;ELLIOT OF KELLYNCH HALL.
+
+&quot;
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>swap F and U 	percent valid English words: 0.8171690435744479 
 
 
 Chapter 1

diff --git a/notebooks/markov-properties.html b/notebooks/markov-properties.html
@@ -639,7 +639,7 @@ <h2>Markov Property:<a class="headerlink" href="#markov-property" title="Permali
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/11886743cb6e2e735fccb2337fdcdeeed3d3fbc601dfc1a84cb7b2b5a3da7fd5.png" src="../_images/11886743cb6e2e735fccb2337fdcdeeed3d3fbc601dfc1a84cb7b2b5a3da7fd5.png" />
+<img alt="../_images/33b5b8500218c0deadd2b3194f23008980bf9d01d580ad7b766f0069cea601e9.png" src="../_images/33b5b8500218c0deadd2b3194f23008980bf9d01d580ad7b766f0069cea601e9.png" />
 </div>
 </div>
 <p><br/><big>This holds true for most real-life markov chain models, no matter the size of the model. For example:</big></p>
@@ -671,15 +671,15 @@ <h2>Markov Property:<a class="headerlink" href="#markov-property" title="Permali
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>P = [[0.2216 0.0798 0.6986]
- [0.5102 0.1542 0.3357]
- [0.3867 0.0355 0.5778]]
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>P = [[0.2528 0.3704 0.3768]
+ [0.213  0.2853 0.5017]
+ [0.2449 0.4089 0.3462]]
 
-(S1, S1): 0.2216		(S1, S2): 0.0798		(S1, S3): 0.6986		(S2, S1): 0.5102		(S2, S2): 0.1542		(S2, S3): 0.3357		(S3, S1): 0.3867		(S3, S2): 0.0355		(S3, S3): 0.5778		
+(S1, S1): 0.2528		(S1, S2): 0.3704		(S1, S3): 0.3768		(S2, S1): 0.213		(S2, S2): 0.2853		(S2, S3): 0.5017		(S3, S1): 0.2449		(S3, S2): 0.4089		(S3, S3): 0.3462		
 </pre></div>
 </div>
-<img alt="../_images/8f7deea221c7e144e3a1668941bc3c29999dae349d5c9abbdc03ec86defce029.png" src="../_images/8f7deea221c7e144e3a1668941bc3c29999dae349d5c9abbdc03ec86defce029.png" />
-<img alt="../_images/e1aedd4566a5a4c4c3dbc24b3b0fe855ec52559250ad1e770f244490bf4f5218.png" src="../_images/e1aedd4566a5a4c4c3dbc24b3b0fe855ec52559250ad1e770f244490bf4f5218.png" />
+<img alt="../_images/65f31d40206d73379196b02de7050e7d5e1a1a9f08a1440b129974cbc14b3592.png" src="../_images/65f31d40206d73379196b02de7050e7d5e1a1a9f08a1440b129974cbc14b3592.png" />
+<img alt="../_images/61bfaed14d66168c719da2e83337bc291afeee567ab97e81610b4253bf3751fa.png" src="../_images/61bfaed14d66168c719da2e83337bc291afeee567ab97e81610b4253bf3751fa.png" />
 </div>
 </div>
 <div class="cell docutils container">
@@ -711,17 +711,17 @@ <h2>Markov Property:<a class="headerlink" href="#markov-property" title="Permali
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>P = [[2.693e-01 3.000e-04 2.600e-01 2.407e-01 2.298e-01]
- [8.040e-02 1.305e-01 3.867e-01 3.138e-01 8.870e-02]
- [2.491e-01 3.625e-01 1.843e-01 6.600e-02 1.382e-01]
- [2.990e-02 2.735e-01 5.621e-01 6.610e-02 6.840e-02]
- [1.419e-01 3.143e-01 2.520e-02 3.435e-01 1.752e-01]]
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>P = [[0.2264 0.27   0.1052 0.1751 0.2233]
+ [0.2459 0.1966 0.1791 0.1951 0.1833]
+ [0.4235 0.0861 0.451  0.0278 0.0116]
+ [0.237  0.1901 0.3617 0.0537 0.1575]
+ [0.2628 0.1998 0.2037 0.2042 0.1295]]
 
-(S1, S1): 0.2693		(S1, S2): 0.0003		(S1, S3): 0.26		(S1, S4): 0.2407		(S1, S5): 0.2298		(S2, S1): 0.0804		(S2, S2): 0.1305		(S2, S3): 0.3867		(S2, S4): 0.3138		(S2, S5): 0.0887		(S3, S1): 0.2491		(S3, S2): 0.3625		(S3, S3): 0.1843		(S3, S4): 0.066		(S3, S5): 0.1382		(S4, S1): 0.0299		(S4, S2): 0.2735		(S4, S3): 0.5621		(S4, S4): 0.0661		(S4, S5): 0.0684		(S5, S1): 0.1419		(S5, S2): 0.3143		(S5, S3): 0.0252		(S5, S4): 0.3435		(S5, S5): 0.1752		
+(S1, S1): 0.2264		(S1, S2): 0.27		(S1, S3): 0.1052		(S1, S4): 0.1751		(S1, S5): 0.2233		(S2, S1): 0.2459		(S2, S2): 0.1966		(S2, S3): 0.1791		(S2, S4): 0.1951		(S2, S5): 0.1833		(S3, S1): 0.4235		(S3, S2): 0.0861		(S3, S3): 0.451		(S3, S4): 0.0278		(S3, S5): 0.0116		(S4, S1): 0.237		(S4, S2): 0.1901		(S4, S3): 0.3617		(S4, S4): 0.0537		(S4, S5): 0.1575		(S5, S1): 0.2628		(S5, S2): 0.1998		(S5, S3): 0.2037		(S5, S4): 0.2042		(S5, S5): 0.1295		
 </pre></div>
 </div>
-<img alt="../_images/426adb3aa8a8ed3b740d3e3a74bd02eb51fe572da6d0c05544516351a34f521d.png" src="../_images/426adb3aa8a8ed3b740d3e3a74bd02eb51fe572da6d0c05544516351a34f521d.png" />
-<img alt="../_images/3c5a5a1ef233ef1a47f960588e333a5f152a0763c3930d5691d2185f99dcb1cc.png" src="../_images/3c5a5a1ef233ef1a47f960588e333a5f152a0763c3930d5691d2185f99dcb1cc.png" />
+<img alt="../_images/51fa432679182924440a64ce65028d1df58927ce869c3770c07f33bcf8c73f67.png" src="../_images/51fa432679182924440a64ce65028d1df58927ce869c3770c07f33bcf8c73f67.png" />
+<img alt="../_images/5c64d9ae6d86ff0e56d136ee8efbf11e4f306d6f99fa12a936c821ae487f01e8.png" src="../_images/5c64d9ae6d86ff0e56d136ee8efbf11e4f306d6f99fa12a936c821ae487f01e8.png" />
 </div>
 </div>
 <p><br/>… even size-105 markov chains, but that will require a lot of computational power. So, let’s jump to a different type of markov chains/states…</p>
@@ -760,17 +760,17 @@ <h3>Absorbing Markov Chains and Absorbing States:<a class="headerlink" href="#ab
 </div>
 <div class="cell_output docutils container">
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>P = [[1.     0.     0.    ]
- [0.4622 0.4667 0.0711]
- [0.285  0.5266 0.1885]]
+ [0.1283 0.6388 0.2328]
+ [0.38   0.3575 0.2624]]
 
-(S1, S1): 1.0		(S2, S1): 0.4622		(S2, S2): 0.4667		(S2, S3): 0.0711		(S3, S1): 0.285		(S3, S2): 0.5266		(S3, S3): 0.1885		
+(S1, S1): 1.0		(S2, S1): 0.1283		(S2, S2): 0.6388		(S2, S3): 0.2328		(S3, S1): 0.38		(S3, S2): 0.3575		(S3, S3): 0.2624		
 </pre></div>
 </div>
-<img alt="../_images/894196520fc9174a74cf993b3b455eb3e086519d72119d2721534549057decfc.png" src="../_images/894196520fc9174a74cf993b3b455eb3e086519d72119d2721534549057decfc.png" />
+<img alt="../_images/e86d6caf36a3d61015095ef2e930b5a6c4fd8307911f620e7c3a6b0207d1dccf.png" src="../_images/e86d6caf36a3d61015095ef2e930b5a6c4fd8307911f620e7c3a6b0207d1dccf.png" />
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>That is, S1 is the Absorbing State.
 </pre></div>
 </div>
-<img alt="../_images/8fe11f4730fd2cc4654b522ce31e703a6a9f9d1d7fd2af92c097c4cb8623cfb8.png" src="../_images/8fe11f4730fd2cc4654b522ce31e703a6a9f9d1d7fd2af92c097c4cb8623cfb8.png" />
+<img alt="../_images/126bb9f77fb9de8d6138c738075eecb126ffb5d25ad9794c4252cdbc0fdc2873.png" src="../_images/126bb9f77fb9de8d6138c738075eecb126ffb5d25ad9794c4252cdbc0fdc2873.png" />
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 <p><br/>Such tranfer matrices have the form of: <span class="math notranslate nohighlight">\(P_{A\ ((m+n)\times(m+n))} = \begin{bmatrix}I_m&amp;O\\R&amp;Q_n\end{bmatrix}\)</span>.</p>

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diff --git a/reports/notebooks/markov-properties.err.log b/reports/notebooks/markov-properties.err.log
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