From 714dd6396988a41798b0f4cd493fb2325f6d924a Mon Sep 17 00:00:00 2001 From: Filip Gralinski Date: Sun, 6 Mar 2022 17:45:21 +0100 Subject: [PATCH] =?UTF-8?q?Uporz=C4=85dkowanie=20wykres=C3=B3w?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- wyk/02_Jezyki.org | 53 ++++++++++----------- wyk/02_Jezyki/binomial-entropy.png | Bin 0 -> 26079 bytes wyk/02_Jezyki/dna-aminos.png | Bin 0 -> 15792 bytes wyk/02_Jezyki/dna-chars.png | Bin 0 -> 12439 bytes wyk/02_Jezyki/dna_length.png | Bin 0 -> 13466 bytes wyk/02_Jezyki/pt-2-word-ngrams-log-log.png | Bin 0 -> 10280 bytes wyk/02_Jezyki/pt-3-char-ngrams-log-log.png | Bin 0 -> 15450 bytes wyk/02_Jezyki/pt-chars.png | Bin 0 -> 25256 bytes wyk/02_Jezyki/pt-lengths.png | Bin 0 -> 42193 bytes wyk/02_Jezyki/pt-words-20.png | Bin 0 -> 17264 bytes wyk/02_Jezyki/pt-words-log-log.png | Bin 0 -> 14454 bytes wyk/02_Jezyki/pt-words-log.png | Bin 0 -> 10602 bytes wyk/02_Jezyki/pt-words.png | Bin 0 -> 12507 bytes wyk/02_Jezyki/voy-chars.png | Bin 0 -> 19446 bytes wyk/02_Jezyki/voy-log-log.png | Bin 0 -> 14043 bytes wyk/02_Jezyki/voy-words-20.png | Bin 0 -> 17097 bytes wyk/02_Jezyki/voy-words-log-log.png | Bin 0 -> 14043 bytes 17 files changed, 26 insertions(+), 27 deletions(-) create mode 100644 wyk/02_Jezyki/binomial-entropy.png create mode 100644 wyk/02_Jezyki/dna-aminos.png create mode 100644 wyk/02_Jezyki/dna-chars.png create mode 100644 wyk/02_Jezyki/dna_length.png create mode 100644 wyk/02_Jezyki/pt-2-word-ngrams-log-log.png create mode 100644 wyk/02_Jezyki/pt-3-char-ngrams-log-log.png create mode 100644 wyk/02_Jezyki/pt-chars.png create mode 100644 wyk/02_Jezyki/pt-lengths.png create mode 100644 wyk/02_Jezyki/pt-words-20.png create mode 100644 wyk/02_Jezyki/pt-words-log-log.png create mode 100644 wyk/02_Jezyki/pt-words-log.png create mode 100644 wyk/02_Jezyki/pt-words.png create mode 100644 wyk/02_Jezyki/voy-chars.png create mode 100644 wyk/02_Jezyki/voy-log-log.png create mode 100644 wyk/02_Jezyki/voy-words-20.png create mode 100644 wyk/02_Jezyki/voy-words-log-log.png diff --git a/wyk/02_Jezyki.org b/wyk/02_Jezyki.org index 97f16d4..d6d46fa 100644 --- a/wyk/02_Jezyki.org +++ b/wyk/02_Jezyki.org @@ -100,7 +100,7 @@ OrderedDict([(' ', 63444), ('a', 30979), ('i', 29353), ('e', 25343), ('o', 23050 plt.bar(freq.keys(), freq.values()) - fname = f'{name}.png' + fname = f'02_Jezyki/{name}.png' plt.savefig(fname) @@ -110,7 +110,7 @@ OrderedDict([(' ', 63444), ('a', 30979), ('i', 29353), ('e', 25343), ('o', 23050 #+END_SRC #+RESULTS: -[[file:]] +[[file:02_Jezyki/pt-chars.png]] *** Słowa @@ -143,7 +143,7 @@ Zobaczmy 20 najczęstszych wyrazów. #+END_SRC #+RESULTS: -[[file:pt-words-20.png]] +[[file:02_Jezyki/pt-words-20.png]] Zobaczmy pełny obraz, już bez etykiet. @@ -157,7 +157,7 @@ Zobaczmy pełny obraz, już bez etykiet. plt.figure().clear() plt.plot(range(1, len(freq.values())+1), freq.values()) - fname = f'{name}.png' + fname = f'02_Jezyki/{name}.png' plt.savefig(fname) @@ -167,7 +167,7 @@ Zobaczmy pełny obraz, już bez etykiet. #+END_SRC #+RESULTS: -[[file:pt-words.png]] +[[file:02_Jezyki/pt-words.png]] Widać, jak różne skale obejmuje ten wykres. Zastosujemy logartm, najpierw tylko do współrzędnej y. @@ -182,7 +182,7 @@ najpierw tylko do współrzędnej y. plt.figure().clear() plt.plot(range(1, len(freq.values())+1), [log(y) for y in freq.values()]) - fname = f'{name}.png' + fname = f'02_Jezyki/{name}.png' plt.savefig(fname) @@ -192,7 +192,7 @@ najpierw tylko do współrzędnej y. #+END_SRC #+RESULTS: -[[file:pt-words-log.png]] +[[file:02_Jezyki/pt-words-log.png]] **Pytanie** Dlaczego widzimy coraz dłuższe „schodki”? @@ -232,7 +232,7 @@ Tę własność tekstów nazywamy **prawem Zipfa**. plt.figure().clear() plt.plot([log(x) for x in range(1, len(freq.values())+1)], [log(y) for y in freq.values()]) - fname = f'{name}.png' + fname = f'02_Jezyki/{name}.png' plt.savefig(fname) @@ -242,7 +242,7 @@ Tę własność tekstów nazywamy **prawem Zipfa**. #+END_SRC #+RESULTS: -[[file:pt-words-log-log.png]] +[[file:02_Jezyki/pt-words-log-log.png]] *** Związek między frekwencją a długością @@ -257,7 +257,7 @@ częstością użycia słowa a jego długością. Generalnie im krótsze słowo, plt.scatter([len(x) for x in freq.keys()], [log(y) for y in freq.values()], facecolors='none', edgecolors='r') - fname = f'{name}.png' + fname = f'02_Jezyki/{name}.png' plt.savefig(fname) @@ -267,7 +267,7 @@ częstością użycia słowa a jego długością. Generalnie im krótsze słowo, #+END_SRC #+RESULTS: -[[file:pt-lengths.png]] +[[file:02_Jezyki/pt-lengths.png]] ** N-gramy @@ -322,7 +322,7 @@ Zawsze powinniśmy się upewnić, czy jest jasne, czy chodzi o n-gramy znakowe c #+END_SRC #+RESULTS: -[[file:pt-3-char-ngrams-log-log.png]] +[[file:02_Jezyki/pt-3-char-ngrams-log-log.png]] *** 2-gramy wyrazowe @@ -331,7 +331,7 @@ Zawsze powinniśmy się upewnić, czy jest jasne, czy chodzi o n-gramy znakowe c #+END_SRC #+RESULTS: -[[file:pt-2-word-ngrams-log-log.png]] +[[file:02_Jezyki/pt-2-word-ngrams-log-log.png]] ** Tajemniczy język Manuskryptu Wojnicza @@ -367,6 +367,7 @@ dramatycznie na analizę statystyczną. #+RESULTS: :results: +9 OR 9FAM ZO8 QOAR9 Q*R 8ARAM 29 [O82*]OM OPCC9 OP :end: #+BEGIN_SRC python :session mysession :results file @@ -374,28 +375,28 @@ dramatycznie na analizę statystyczną. #+END_SRC #+RESULTS: -[[file:voy-chars.png]] +[[file:02_Jezyki/voy-chars.png]] #+BEGIN_SRC python :session mysession :results file log_rang_log_freq('voy-log-log', get_words(voynich)) #+END_SRC #+RESULTS: -[[file:voy-log-log.png]] +[[file:02_Jezyki/voy-log-log.png]] #+BEGIN_SRC python :session mysession :results file rang_freq_with_labels('voy-words-20', get_words(voynich), top=20) #+END_SRC #+RESULTS: -[[file:voy-words-20.png]] +[[file:02_Jezyki/voy-words-20.png]] #+BEGIN_SRC python :session mysession :results file log_rang_log_freq('voy-words-log-log', get_words(voynich)) #+END_SRC #+RESULTS: -[[file:voy-words-log-log.png]] +[[file:02_Jezyki/voy-words-log-log.png]] ** Język DNA @@ -427,7 +428,7 @@ TATAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCC #+END_SRC #+RESULTS: -[[file:dna-chars.png]] +[[file:02_Jezyki/dna-chars.png]] *** Tryplety — znaczące cząstki genotypu @@ -463,7 +464,7 @@ z dwudziestu aminokwasów. #+END_SRC #+RESULTS: -[[file:dna-aminos.png]] +[[file:02_Jezyki/dna-aminos.png]] *** „Zdania” w języku DNA @@ -484,7 +485,7 @@ trypletu STOP (_ powyżej). Taka sekwencja to /gen/. plt.figure().clear() plt.hist([len(g) for g in get_genes(get_triplets(dna))], bins=100) - fname = 'dna_length.png' + fname = '02_Jezyki/dna_length.png' plt.savefig(fname) @@ -492,7 +493,7 @@ trypletu STOP (_ powyżej). Taka sekwencja to /gen/. #+END_SRC #+RESULTS: -[[file:dna_length.png]] +[[file:02_Jezyki/dna_length.png]] ** Entropia @@ -656,7 +657,7 @@ Wiemy już, że entropia dla rzutu monetą wynosi 1 bit. A jaki będzie wynik dl plt.ylabel('entropia') plt.plot(x, y) - fname = f'binomial-entropy.png' + fname = f'02_Jezyki/binomial-entropy.png' plt.savefig(fname) @@ -664,7 +665,7 @@ Wiemy już, że entropia dla rzutu monetą wynosi 1 bit. A jaki będzie wynik dl #+END_SRC #+RESULTS: -[[file:binomial-entropy.png]] +[[file:02_Jezyki/binomial-entropy.png]] *Pytanie* Dla oszukańczej monety (np. dla której wypada zawsze orzeł) entropia wynosi 0, czy to wynik zgodny z intuicją? @@ -743,11 +744,9 @@ Policzmy entropię przy takim założeniu: #+RESULTS: :results: -4.973808176335181 +3.902708104423842 :end: -Wartość zaskakująco zbliżona do „Pana Tadeusza”! - *** Rzeczywista entropia? 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