From 6a43b66fca4a79ad84e18cc2a1ccd5516ce56a5e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Piotrek=20Gabry=C5=9B?= Date: Wed, 3 Jun 2020 18:31:12 +0200 Subject: [PATCH 01/25] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 3c4d351..017174c 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -# [WIP] mtd-learn: Package for training Mixture Transition Distribution (MTD) models +# mtd-learn: Package for training Mixture Transition Distribution (MTD) models ## Installation From be9bcaf3a09f2f2d752b8f49cfeb5f587d5fd039 Mon Sep 17 00:00:00 2001 From: PiotrGabrys Date: Thu, 4 Jun 2020 22:50:59 +0200 Subject: [PATCH 02/25] install from PyPI --- README.md | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/README.md b/README.md index 3c4d351..1a3a08b 100644 --- a/README.md +++ b/README.md @@ -3,9 +3,7 @@ ## Installation ``` -git clone git@github.com:PiotrekGa/mtd-learn.git -cd mtd-learn -pip install . +pip install mtdlearn ``` ## Communication From 8c135a3e190fbd8e836a3a0ac772d5c388d4e61b Mon Sep 17 00:00:00 2001 From: PiotrGabrys Date: Thu, 4 Jun 2020 22:55:25 +0200 Subject: [PATCH 03/25] version up for PyPI --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 90f52fc..949828c 100644 --- a/setup.py +++ b/setup.py @@ -8,7 +8,7 @@ author='Piotr Gabryś', author_email='piotrek.gabrys@gmail.com', description='Package for training Mixture Transition Distribution (MTD) models', - version='0.2.2', + version='0.2.3', packages=find_packages(), install_requires=[ 'joblib>=0.11', From 61d549dcf89375615947fe62ba40aef3a1e3fa01 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Piotrek=20Gabry=C5=9B?= Date: Fri, 5 Jun 2020 15:38:27 +0200 Subject: [PATCH 04/25] Update README.md --- README.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/README.md b/README.md index 42a2743..0ed20e0 100644 --- a/README.md +++ b/README.md @@ -6,6 +6,10 @@ pip install mtdlearn ``` +## Usage examples + +Foe usage examples please refer to [examples](https://github.com/PiotrekGa/mtd-learn/tree/master/examples) section. + ## Communication GitHub [Issues](https://github.com/PiotrekGa/mtd-learn/issues) for bug reports, feature requests and questions. From f96d03be3e9d6b1eb1c17fc4c28a48b6fdde1ba6 Mon Sep 17 00:00:00 2001 From: PiotrGabrys Date: Fri, 5 Jun 2020 18:35:34 +0200 Subject: [PATCH 05/25] added an image to readme --- README.md | 4 ++++ readme_images/mtd.png | Bin 0 -> 30968 bytes 2 files changed, 4 insertions(+) create mode 100644 readme_images/mtd.png diff --git a/README.md b/README.md index 0ed20e0..bf4170c 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,9 @@ # mtd-learn: Package for training Mixture Transition Distribution (MTD) models +## MTD models + +![mtd](readme_images/mtd.png) + ## Installation ``` diff --git a/readme_images/mtd.png b/readme_images/mtd.png new file mode 100644 index 0000000000000000000000000000000000000000..edbb73ab51e7b8240b2d11c20120ea665054bf11 GIT binary patch literal 30968 zcmeFZ2T)Vp`Yw*21ySiil&aJ~6h!G&lwJ+J2BmkTcTiDEXi`G2q1Q;S0Sf^E0qGr- z4xvNnkpD)H-#O=Z&;QPyxpQZJcjji#BzuRoR`y=+yViQ%_jwi}&s1cIuhCp1ARr)? zmy=Q>Ah--CAh^JN^$PF})(zJ~K;Y3VFD0qrF}8{(jink)-Ovvx;dKd}yj1n&`O=+( 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zIIRh$z^^f9Qop?<-MgRmPi5y(d2W;*g)@@Nb4(fBcKHd%^?kf@UcaoWOebrY&!wKW zj$zJ)`I<|$NT$tvNdf4ew{!PezMbPW|8X5I#JLg`gITzDbL?j7`>&cTtmjv9MU8y~ z7KBtroUc%wkStG9aqJR0d(hhdQ=OXh3`$nVy}SKi87+^3axMHZ4tcSY>Us#3w$PyE zFwp<^hn`+aEG#VSbZxHA_PTaS`fOF!GpCksiuq5ZeiryOW{-=z0(d@Al-;JDqG9=5+p Date: Fri, 5 Jun 2020 19:08:02 +0200 Subject: [PATCH 06/25] formulas added --- README.md | 5 ++++- readme_images/mtd_contraints.png | Bin 0 -> 15904 bytes readme_images/mtd_def.png | Bin 0 -> 12691 bytes 3 files changed, 4 insertions(+), 1 deletion(-) create mode 100644 readme_images/mtd_contraints.png create mode 100644 readme_images/mtd_def.png diff --git a/README.md b/README.md index bf4170c..86a3dd8 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,10 @@ ## MTD models -![mtd](readme_images/mtd.png) +![mtd_def](readme_images/mtd_def.png) +![mtd_constr](readme_images/mtd_contraints.png) + +![mtd_img](readme_images/mtd.png) ## Installation diff --git a/readme_images/mtd_contraints.png b/readme_images/mtd_contraints.png 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z^q1!Qz0vy!UtCdO`5gFXSa7fnb`+%JA)uokCnuMb{4?rzv>9beK0G>_5B*(Gu?;D~ zs+vK4a44jO$mH1-&c9P+c(mvcDYrwTdwy{d6*_cK$RPX7pd8_@ySuwX)R-Lh^zl4j zymKqM@z1E9Sq)ty+(gsav3r-r4% zKVy2FkkUAeS6nzdJ0s_@BMA}R@a4u02WNm6YC19eXj!#W3L14xod?qAvv`joB0CWIl&<>NA zL+g9|*B%;Krbp?&fVlwd$S~~pS8VeA|5O1dZa9q9!RBh4oTPwY5=q>UJ-AF@Ls;R5~lXPSxEQ9q4sab_e> Date: Fri, 5 Jun 2020 22:49:40 +0200 Subject: [PATCH 08/25] params table added --- README.md | 31 +++++++++++++++++++++++++++++++ 1 file changed, 31 insertions(+) diff --git a/README.md b/README.md index 6e87abc..be4fa3c 100644 --- a/README.md +++ b/README.md @@ -2,12 +2,43 @@ ## MTD models +### Definition + ![mtd_def](readme_images/mtd_def_small.png) +Where _lambdas_ are lag parameters and Qg = [qigi0(g)] +is a _m_ x _m_ transition matrix representing relationship between _g_ lag and the present state. + +To parameters have to meet following contraints to produce probabilities: + ![mtd_constr](readme_images/mtd_contraints_small.png) ![mtd_img](readme_images/mtd.png) +### Number of independent parameters + +Comparison of parameters number between Markov Chain and MTD model. + +| States | Order | Markov Chain | MTD[1] | +|----------|:-------------:|-------------:|--------------------:| +| 2 | 1 | 2 | 2 | +| 2 | 2 | 4 | 3 | +| 2 | 3 | 8 | 4 | +| 2 | 4 | 16 | 5 | +| 3 | 1 | 6 | 6 | +| 3 | 2 | 18 | 10 | +| 3 | 3 | 54 | 14 | +| 3 | 4 | 162 | 18 | +| 5 | 1 | 20 | 20 | +| 5 | 2 | 100 | 36 | +| 5 | 3 | 500 | 52 | +| 5 | 4 | 2500 | 68 | +| 10 | 1 | 90 | 90 | +| 10 | 2 | 900 | 171 | +| 10 | 3 | 9000 | 252 | +| 10 | 4 | 90000 | 333 | + + ## Installation ``` From 9a019f4173d384adc9c08d2372b4587ee78a084c Mon Sep 17 00:00:00 2001 From: PiotrGabrys Date: Fri, 5 Jun 2020 22:50:48 +0200 Subject: [PATCH 09/25] smaller image --- readme_images/mtd_def_small.png | Bin 13921 -> 13346 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/readme_images/mtd_def_small.png b/readme_images/mtd_def_small.png index 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a/README.md +++ b/README.md @@ -4,6 +4,11 @@ ### Definition +The Mixture Transition Distribution (MTD) model was proposed in 1985 by Raftery[1]. It aimed to approximate +higher order Markov Chains, but can be used as a standalone model. + +Definition: + ![mtd_def](readme_images/mtd_def_small.png) Where _lambdas_ are lag parameters and Qg = [qigi0(g)] @@ -13,6 +18,8 @@ To parameters have to meet following contraints to produce probabilities: ![mtd_constr](readme_images/mtd_contraints_small.png) +The model can be easier understood as weighted probabilities (by _lambdas_) of Qg matrices: + ![mtd_img](readme_images/mtd.png) ### Number of independent parameters @@ -47,7 +54,7 @@ pip install mtdlearn ## Usage examples -Foe usage examples please refer to [examples](https://github.com/PiotrekGa/mtd-learn/tree/master/examples) section. +For usage examples please refer to [examples](https://github.com/PiotrekGa/mtd-learn/tree/master/examples) section. ## Communication GitHub [Issues](https://github.com/PiotrekGa/mtd-learn/issues) for bug reports, feature requests and questions. 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dD!y`r`21SxqLr!@f%9vKl$gBe`@0W({|jv&zRv&v From 41efa0fe7c24c071fb48f91af6436f843625b478 Mon Sep 17 00:00:00 2001 From: PiotrGabrys Date: Sat, 6 Jun 2020 12:05:34 +0200 Subject: [PATCH 15/25] readme update --- README.md | 3 ++- readme_images/mtd.png | Bin 30968 -> 24217 bytes 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 86c5b9f..3233024 100644 --- a/README.md +++ b/README.md @@ -18,7 +18,8 @@ To parameters have to meet following contraints to produce probabilities: ![mtd_constr](readme_images/mtd_contraints_small.png) -The model can be easier understood as weighted probabilities (by _lambdas_) of Qg matrices: +The model can be easier understood as weighted probabilities (by _lambdas_) of Qg matrices. The example below +shows how to calculate a probability of transition B->C->A->B from an order 3 MTD model: ![mtd_img](readme_images/mtd.png) diff --git a/readme_images/mtd.png b/readme_images/mtd.png index edbb73ab51e7b8240b2d11c20120ea665054bf11..884f234513b65f0a4c2fcf3fb5469fad6f92fd69 100644 GIT binary patch literal 24217 zcmd422Q-}P+Bc3=L?jXfK_pt#1R+MWNHC*D8H~0Qb@Uo7NJB`X579+N9itASjM<5Z z7}18&%a#N&gNZWQ82*p!v(MS@|IIn?x7PQ4YkeN;Sv+^U?(4qp_WNB|tg)dE8w)QB z0|NsaSof9*1H)l7@b~)hqrls0leovgVD}sNmZn+o*b$k7vVNoE}2+gfKOua=1ac12U~MCtbe7B&g(mV6O`>q4SU&sc}wMSoN< zVXq5@oT(9JHc*ysNFVnZer;PG_V%R#SU#w=Hcv2Y;|gUuW3#t~wq`}P`q3)Cwj4B- z;gUPOmNC8dVxAEY>i_f)=1R&F!t&nqnN+XM{nwfq?|Tm!n~vG?WA=@n6FcrlTzr_> z@JQ=YrHkmG!Oyl`8EiF`7nwF*YASQ3p3$_LO%r6n---}@m{~0(u7|bLEN=?>osQ~R zz1MJ_!L9Aqa8F(1up8~c=Dy;v+Wi&zpPd*y8u-BCuu~mPDPq`?;_bU{|CMR0u;Qx+ zr5}E@oweH89^ryM7?Fi7$MR+ zG1g=FE6G)%**9TW$%dN&@8F57FbMJ--__-Fy=ciH2@3l9*)mIM!1K`0c6j^-k4&w^ z4lc>8YHp4p&9lhHLCv0+__>u=B^5P3Auz{neTqIj@M+%JgZeL6pA8U{u3D6Cuz5u9(Txd4 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GK$9Oe)eJY#=eX3X&;%Bk ztrG+(N_GA$l~zpN_8DK<9(`v8WJVdm<|ru($Ha7w)7`+L)@HbJOv+?j)Q{UZzp&Lw~$;pp73$RsSy?@UOc6 zuUa{@??EFYG_d>c_xhiLW;;RZPYU0kj{a@`IU5I(%7IMwr=$O>m;9qeNGJQhqA0Tz zq_X{87TtUL#z7|`$!32m?LL!MOGuwh_hvTXqjT54;zT}euIPZ4oM zd-3hCT>187ck&Gf)vGa7>^`=;-O00CtGl*K;dgQ?Cl7_Qw3LK!W>Yf>XHlm6nFO9b zefsHXW+rlRaZ@`8SJ3J6!eW{;U#5E+F5W#6M9wS@In|RQ9r)xJzmuKgqaZrIX!Yy0 zIM3avHufb>JHd{ro#ZoH?w{M_9k_O1uY0dAI$Kv~WM(2$Mxs&?g;vDq5F z&I_(qHI?ukpM_C-6Ca z{Bq;`)_cK+Y@Tm|x;l3xL`B!j6-_KHEi=7FXNO&q#OzUyBaq3hjg{S3A*4s>+4&p5 z=8?uaz<%t|q1)5`#wqZ>2I@N@{OCu<&1%_z_?djC5_zKkFEyX;Zc)6gAnrU{kZbE`)tOy ztidVt^uDzjpDhvT`QcoBnqfp~#loUpybbdO6ORvI0C)F9d%qlSGHG!1QL*O*d++RBoL>v!0V(yHiJW-}eUM2u_2ZtWn# zmncZ5quZ;sR~e_Nk-W*7gvpP@NW&9h%p8yb%m~i+o^KPA|HIP^NLz^C$hCZWQl0W& z+N)e;N}LDVq9ZgWRs6trM2rPrvSExQzys5n6 zbSjSq#MwvJ_p08)QVb$UTP=0Xs*q*^zfjTU!g5W732B6#HX?XQQa3p|x-cqzwBxRm z`se48<+)n>f-5R;JjZotbQUw}9s3sj8Uybx>y&NibX4Pin`fh2pBH}hYFy3Bd*&RR z3v`=LmY|>TL3*)MdQZ@mT_H}RnL>*iOe}bsiez}{=G!9tg>idd?2u%g4}U&Q{bSoq zIIRh$z^^f9Qop?<-MgRmPi5y(d2W;*g)@@Nb4(fBcKHd%^?kf@UcaoWOebrY&!wKW zj$zJ)`I<|$NT$tvNdf4ew{!PezMbPW|8X5I#JLg`gITzDbL?j7`>&cTtmjv9MU8y~ z7KBtroUc%wkStG9aqJR0d(hhdQ=OXh3`$nVy}SKi87+^3axMHZ4tcSY>Us#3w$PyE zFwp<^hn`+aEG#VSbZxHA_PTaS`fOF!GpCksiuq5ZeiryOW{-=z0(d@Al-;JDqG9=5+p Date: Sat, 6 Jun 2020 12:30:25 +0200 Subject: [PATCH 16/25] readme update --- README.md | 16 +++++++--------- 1 file changed, 7 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index 3233024..5e5a4a1 100644 --- a/README.md +++ b/README.md @@ -1,13 +1,17 @@ # mtd-learn: Package for training Mixture Transition Distribution (MTD) models -## MTD models +## Installation -### Definition +``` +pip install mtdlearn +``` + +## MTD model The Mixture Transition Distribution (MTD) model was proposed in 1985 by Raftery[1]. It aimed to approximate higher order Markov Chains, but can be used as a standalone model. -Definition: +### Definition ![mtd_def](readme_images/mtd_def_small.png) @@ -47,12 +51,6 @@ Comparison of parameters number between Markov Chain and MTD model. | 10 | 4 | 90000 | 333 | -## Installation - -``` -pip install mtdlearn -``` - ## Usage examples For usage examples please refer to [examples](https://github.com/PiotrekGa/mtd-learn/tree/master/examples) section. From bb3bb5cd013f38ca10b8aec4b237ebd1c57d3e6c Mon Sep 17 00:00:00 2001 From: PiotrGabrys Date: Sat, 13 Jun 2020 22:10:03 +0200 Subject: [PATCH 17/25] readme update --- README.md | 17 +++++++++-------- 1 file changed, 9 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 5e5a4a1..594ded5 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -# mtd-learn: Package for training Mixture Transition Distribution (MTD) models +# mtd-learn: Package for training Generalized Mixture Transition Distribution (MTDg) models ## Installation @@ -6,10 +6,10 @@ pip install mtdlearn ``` -## MTD model +## MTDg model -The Mixture Transition Distribution (MTD) model was proposed in 1985 by Raftery[1]. It aimed to approximate -higher order Markov Chains, but can be used as a standalone model. +The Generalized Mixture Transition Distribution (MTDg) model was proposed in 1985 by Raftery[1]. It aimed +to approximate higher order Markov Chains, but can be used as a standalone model. ### Definition @@ -18,7 +18,7 @@ higher order Markov Chains, but can be used as a standalone model. Where _lambdas_ are lag parameters and Qg = [qigi0(g)] is a _m_ x _m_ transition matrix representing relationship between _g_ lag and the present state. -To parameters have to meet following contraints to produce probabilities: +To parameters have to meet following constraints to produce probabilities: ![mtd_constr](readme_images/mtd_contraints_small.png) @@ -29,9 +29,10 @@ shows how to calculate a probability of transition B->C->A->B from an order 3 MT ### Number of independent parameters -Comparison of parameters number between Markov Chain and MTD model. +The number independent parameters of the MTDg model equals _(ml - m + 1)(l - 1)_ and for Markov Chain +_ml(m-1)_. You can find a comparison of the number of parameters below. -| States | Order | Markov Chain | MTD[1] | +| States | Order | Markov Chain | MTDg[1] | |----------|:-------------:|-------------:|--------------------:| | 2 | 1 | 2 | 2 | | 2 | 2 | 4 | 3 | @@ -65,6 +66,6 @@ Any contribution is welcome! Please follow this [branching model](https://nvie.c MIT License (see [LICENSE](https://github.com/PiotrekGa/mtd-learn/blob/master/LICENSE)). ## References -1. Introduction to MTD model [The Mixture Transition Distribution Model for High-Order Markov Chains and Non-Gaussian Time Series](https://projecteuclid.org/euclid.ss/1042727943) by André Berchtold and Adrian Raftery +1. Introduction to MTDg model [The Mixture Transition Distribution Model for High-Order Markov Chains and Non-Gaussian Time Series](https://projecteuclid.org/euclid.ss/1042727943) by André Berchtold and Adrian Raftery 2. Paper with estimation algorithm implemented in the package [An EM algorithm for estimation in the Mixture Transition Distribution model ](https://arxiv.org/abs/0803.0525) by Sophie Lèbre and Pierre-Yves Bourguinon. From a8ca9ad433ef403acd7f3ce2de59818580e05818 Mon Sep 17 00:00:00 2001 From: PiotrGabrys Date: Sat, 13 Jun 2020 22:11:02 +0200 Subject: [PATCH 18/25] readme update --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 594ded5..61bb432 100644 --- a/README.md +++ b/README.md @@ -23,7 +23,7 @@ To parameters have to meet following constraints to produce probabilities: ![mtd_constr](readme_images/mtd_contraints_small.png) The model can be easier understood as weighted probabilities (by _lambdas_) of Qg matrices. The example below -shows how to calculate a probability of transition B->C->A->B from an order 3 MTD model: +shows how to calculate a probability of transition B->C->A->B from an order 3 MTDg model: ![mtd_img](readme_images/mtd.png) From a121edd9d43afd70ff97e4fad45c14c6ecab6109 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Piotrek=20Gabry=C5=9B?= Date: Fri, 17 Jul 2020 22:22:23 +0200 Subject: [PATCH 19/25] Update 2020-07-06-Mixture-Transition-Distribution.md --- post/2020-07-06-Mixture-Transition-Distribution.md | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/post/2020-07-06-Mixture-Transition-Distribution.md b/post/2020-07-06-Mixture-Transition-Distribution.md index fca718f..703cb16 100644 --- a/post/2020-07-06-Mixture-Transition-Distribution.md +++ b/post/2020-07-06-Mixture-Transition-Distribution.md @@ -1,6 +1,4 @@ --- -published: true ---- # 2020-07-06 Mixture Transition Distribution models _Model explanation and usage example_ @@ -528,4 +526,4 @@ and Non-Gaussian Time Series , 2002., Statistical Science Vol. 17, No. 3, 328-35 2. LEBRE, BOURGUIGNON, An EM algorithm for estimation in the Mixture Transition Distribution model , Laboratoire Statistique et Genome, Universite Evry Val d'Essonne, Evry, 2009 3. BERCHTOLD, Estimation of the Mixture Transition Distribution Model . 1999, Technical Report no. 352, Department of -Statistics, University of Washington \ No newline at end of file +Statistics, University of Washington From ef4297c459d7ec177368d7bf910e4fe60a1b27fb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Piotrek=20Gabry=C5=9B?= Date: Fri, 17 Jul 2020 22:28:38 +0200 Subject: [PATCH 20/25] Update README.md --- README.md | 22 +++++++++++++++++++++- 1 file changed, 21 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 61bb432..39e382b 100644 --- a/README.md +++ b/README.md @@ -53,8 +53,28 @@ _ml(m-1)_. You can find a comparison of the number of parameters belo ## Usage examples +``` +from mtdlearn.mtd import MTD +from mtdlearn.preprocessing import PathEncoder +from mtdlearn.datasets import ChainGenerator + +## Generate data + +cg = ChainGenerator(('A', 'B', 'C'), 3, min_len=4, max_len=5) +x, y = cg.generate_data(1000) + +## Encode paths -For usage examples please refer to [examples](https://github.com/PiotrekGa/mtd-learn/tree/master/examples) section. +pe = PathEncoder(3) +pe.fit(x, y) +x_tr3, y_tr3 = pe.transform(x, y) + +## Fitting model + +model = MTD(order=3) +model.fit(x_tr3, y_tr3) +``` +For more usage examples please refer to [examples](https://github.com/PiotrekGa/mtd-learn/tree/master/examples) section. ## Communication GitHub [Issues](https://github.com/PiotrekGa/mtd-learn/issues) for bug reports, feature requests and questions. From a28f230d12a84dec17589e2d9bea0364b2744ccc Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Piotrek=20Gabry=C5=9B?= Date: Fri, 17 Jul 2020 23:01:57 +0200 Subject: [PATCH 21/25] Update 2020-07-06-Mixture-Transition-Distribution.md --- post/2020-07-06-Mixture-Transition-Distribution.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/post/2020-07-06-Mixture-Transition-Distribution.md b/post/2020-07-06-Mixture-Transition-Distribution.md index 703cb16..ae2d47e 100644 --- a/post/2020-07-06-Mixture-Transition-Distribution.md +++ b/post/2020-07-06-Mixture-Transition-Distribution.md @@ -181,7 +181,7 @@ The MTDg model is a sequence of random variables _(Xn)_ such that:

where _it...i0 ∈ N_, 𝜆 = (𝜆 l,...,𝜆 1)T_ is a weight vector and -_Qg = [q(g)igi0]_ is a _m ⨯ m_ matrix representing association +_Qg = [q(g)igi0]_ is an _m ⨯ m_ matrix representing the association between _g_ lag and the current state. Following conditions has to be met for the model to produce probabilities: From 25be24f952d78a6f2606767de24e9568acbeaea0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Piotrek=20Gabry=C5=9B?= Date: Fri, 17 Jul 2020 23:03:50 +0200 Subject: [PATCH 22/25] Update 2020-07-06-Mixture-Transition-Distribution.md --- post/2020-07-06-Mixture-Transition-Distribution.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/post/2020-07-06-Mixture-Transition-Distribution.md b/post/2020-07-06-Mixture-Transition-Distribution.md index ae2d47e..50abdde 100644 --- a/post/2020-07-06-Mixture-Transition-Distribution.md +++ b/post/2020-07-06-Mixture-Transition-Distribution.md @@ -177,7 +177,7 @@ _Xt-l = il,...,Xt-1 = i1,Xtn)_ such that:

- +

where _it...i0 ∈ N_, 𝜆 = (𝜆 l,...,𝜆 1)T_ is a weight vector and @@ -187,7 +187,7 @@ between _g_ lag and the current state. Following conditions has to be met for the model to produce probabilities:

- +

From 154778c5ac0912c26c585ba34fab0f66ff6fa7d5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Piotrek=20Gabry=C5=9B?= Date: Fri, 17 Jul 2020 23:25:04 +0200 Subject: [PATCH 23/25] Update 2020-07-06-Mixture-Transition-Distribution.md --- post/2020-07-06-Mixture-Transition-Distribution.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/post/2020-07-06-Mixture-Transition-Distribution.md b/post/2020-07-06-Mixture-Transition-Distribution.md index 50abdde..83ff4a3 100644 --- a/post/2020-07-06-Mixture-Transition-Distribution.md +++ b/post/2020-07-06-Mixture-Transition-Distribution.md @@ -345,10 +345,10 @@ Let's analyze change patterns in the exchange rate between US Dollars and Euro b can find the dataset [here](https://github.com/PiotrekGa/mtd-learn/blob/master/examples/euro_usd.csv). Since the MTDg model work on discrete states the changes were binned into four groups: -1. 0_BIG_DROP - more that 0.5% drop +1. 0_BIG_DROP - more than 0.5% drop 2. 1_DROP - less than 0.5% drop -3. 2_RISE - less that 0.5% rise -4. 3_BIG_RISE - more that 0.5% rise +3. 2_RISE - less than 0.5% rise +4. 3_BIG_RISE - more than 0.5% rise Let's start with imports: From e0897aa291970671a37be4301d444124bd38e591 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Piotrek=20Gabry=C5=9B?= Date: Sat, 18 Jul 2020 14:39:36 +0200 Subject: [PATCH 24/25] Update 2020-07-06-Mixture-Transition-Distribution.md --- post/2020-07-06-Mixture-Transition-Distribution.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/post/2020-07-06-Mixture-Transition-Distribution.md b/post/2020-07-06-Mixture-Transition-Distribution.md index 83ff4a3..6a3d05d 100644 --- a/post/2020-07-06-Mixture-Transition-Distribution.md +++ b/post/2020-07-06-Mixture-Transition-Distribution.md @@ -16,7 +16,7 @@ maintained by Andre Berchtold. The Mixture Transition Distribution (MTD) model was proposed by Raftery in 1985[1]. Its initial intent was to approximate high order Markov Chains (MC), but it can serve as an independent model too. The main advantage of -the MTD model is that its number of independent parameters of the MTD model grows linearly with the order in contrast to +the MTD model is that its number of independent parameters grows linearly with the order in contrast to the exponential growth of Markov Chains models. From 3c1fb8b53e76194e1812216fd8ded5fbda32f2e7 Mon Sep 17 00:00:00 2001 From: Matthias Schilling Date: Mon, 11 Sep 2023 16:13:15 +0200 Subject: [PATCH 25/25] np.int -> np.int64 as np.int is deprecated (#14) numpy int type update --- mtdlearn/mtd/src.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/mtdlearn/mtd/src.py b/mtdlearn/mtd/src.py index b653a74..139def7 100644 --- a/mtdlearn/mtd/src.py +++ b/mtdlearn/mtd/src.py @@ -62,7 +62,7 @@ def _aggregate_chain(self, sample_weight: Optional[np.ndarray] = None) -> np.ndarray: if sample_weight is None: - sample_weight = np.ones(x.shape[0], dtype=np.int) + sample_weight = np.ones(x.shape[0], dtype=np.int64) n_columns = x.shape[1] values_dict = {i: 0 for i in range(self._n_dimensions ** (x.shape[1]))} @@ -292,7 +292,7 @@ def __init__(self, min_gain: float = 0.1, lambdas_init: Optional[np.ndarray] = None, transition_matrices_init: Optional[np.ndarray] = None, - verbose: np.int = 1, + verbose: np.int64 = 1, n_jobs: int = -1) -> None: super().__init__(order)