GPy/__init__.py,sha256=-ltakwriKS6QpYB99hGH4hnBFREX23rL3FcT8HZ3Ekw,1384
GPy/__version__.py,sha256=S5bBAK8bL7bybaXGJQuNE98fa3H65zGjTASMiyKGJGw,22
GPy/defaults.cfg,sha256=9IP5-AXalwazWX7EWzd2-BTAfQ5ZZJTYzgV0QG81FCA,867
GPy/installation.cfg,sha256=FvvZ03dmKOOJiGIYg7_0Unhm9T10G7i6JUF8vU1Aq18,61
GPy/core/__init__.py,sha256=J40LL-38ZxdsMRDjkfwJclxDXa6_U7LVOcF3-n_7BGI,439
GPy/core/gp.py,sha256=gfknGTImeKdLDP6MkGL6Dle_nNExHZwPVCCADIZxbEc,37031
GPy/core/mapping.py,sha256=8_I2NxiIR9mEV5INMpFS2riVfuWeSXf5e7kNnZFUjRA,1166
GPy/core/model.py,sha256=aJjYc7ygRDrEiXanodLoQHSoCVHxn0ve7rxJzSw6uiQ,19413
GPy/core/sparse_gp.py,sha256=d7R-p6GbZn-Q-RbzUDXCB2NzwHk4liPAhCpdeIqsDgE,8908
GPy/core/sparse_gp_mpi.py,sha256=-Wf2k-dVinKOWfbFZ-ennT4Trs4mIjx8G_lXUfTYSq4,5099
GPy/core/svgp.py,sha256=N-hkCKsDBxTpdqiLM-kv6QQ1k4Za7YfERulyWtWxoXU,4620
GPy/core/symbolic.py,sha256=33_DAoyeYpfRZ4RoaChAby3I7mQjCmuCut75IQ5i8FA,20434
GPy/core/verbose_optimization.py,sha256=aIeW2O1DiSyv-wJQjle-a_deejY_paukit58YzogPqg,8492
GPy/core/parameterization/__init__.py,sha256=igaz_WVyRPgxW9K-rgSVes_d9B_shZa08mn5Im0jnHM,187
GPy/core/parameterization/domains.py,sha256=rdyyw8zWbYfsKizHDxvxUB-O8W3NgI_a2gyEuk9mEXg,851
GPy/core/parameterization/index_operations.py,sha256=8FIBa4-zesWb2B94qyQAgwziHpZATTr1tFBE6aqGiy8,10061
GPy/core/parameterization/lists_and_dicts.py,sha256=XcCoHTor2Msp2ZcyeleQDla5tosggibeAtlFYzL6mqQ,3895
GPy/core/parameterization/observable.py,sha256=rX_jXC_86yTz56ErwsCIU3kBqT9a50v93FL7e-opJLc,2708
GPy/core/parameterization/observable_array.py,sha256=Y4xccdJ2fkVGnFNijnbB2dAcVxl7iwp1r-jJHfx5b1A,4411
GPy/core/parameterization/param.py,sha256=PrbSCe_j0PYev2kZpu4j82qifHvlHUmNhzIo2pNJw50,23295
GPy/core/parameterization/parameter_core.py,sha256=IbtChNxspfdo7MboVxh9hOLpXtnB9p9NX9nJsu0insk,44692
GPy/core/parameterization/parameterized.py,sha256=hwMCEtSlf_PnmYDbRV3xaY2tBc8ZbDypiahFxUXX1YA,19656
GPy/core/parameterization/priors.py,sha256=UxIwwCMDvvu8t0vg9iywKs5I-qNtQgzU2vguzb6X_HE,45900
GPy/core/parameterization/ties_and_remappings.py,sha256=wQk6EgE-Om_y1TtnKE1thNx6ELwhf9iWG3i7vhPO2MY,9393
GPy/core/parameterization/transformations.py,sha256=gmZuUWJ6gt-b46WoUiRPM6ul6Dc8pEZkUcOMIYgwbsE,20673
GPy/core/parameterization/updateable.py,sha256=_8_zaW6nUUS6n7_Zo5Jn6aatP3o5egYsPtePsaKIeYs,1792
GPy/core/parameterization/variational.py,sha256=05XDFKy_GU8-He674tI_EFV_-OMPnjnOhPQJqtFFFM8,10209
GPy/examples/__init__.py,sha256=HI2cDtu2EfcrH-NKgx8uv0swmolWfs7Ec0PcYmfrJzo,239
GPy/examples/classification.py,sha256=r_uPT_DWZfUmHRRkRKDH5Ull-MNjkysouFCs_w3H_jc,6777
GPy/examples/coreg_example.py,sha256=5ZpCVnP0MDmQIszWrgeBuWIE7CabFOGC2cmulYUmzys,2121
GPy/examples/dimensionality_reduction.py,sha256=9S-uofUaygC8fL1XSFVGqvSFcUMo77z8XlU1urqMSqA,23628
GPy/examples/non_gaussian.py,sha256=-yf8bCPiYD_e26pUNF53xcyl6rYBFTcsEOXT5OsPTCo,10700
GPy/examples/regression.py,sha256=8_-pdXl6aZmssYKCzYLETwPZB-kpTCJybuFdMJzZ5ps,18746
GPy/inference/__init__.py,sha256=UHOdHZbP3Fr6GtDnO-Xva1u7DvJdgAOVcqO8VZE2mmo,86
GPy/inference/latent_function_inference/__init__.py,sha256=IvMtl1Cnvj0jeX91I1PJEiAVp-BegPmU6gjUJsY4lqw,3440
GPy/inference/latent_function_inference/dtc.py,sha256=VU0M4rDbq-ibuo_2CJy3x4LtG5t1g3Rpibqs_8ULjLs,5661
GPy/inference/latent_function_inference/exact_gaussian_inference.py,sha256=tOEBblPCZHzle8Xt8m06HZ0oY83nP_TOEV-WUe-frBU,2785
GPy/inference/latent_function_inference/expectation_propagation.py,sha256=dpHmHTgpmxM-uH98Nrkt-mIk5CrG1F7vQ9ZCSTyM8Xo,5422
GPy/inference/latent_function_inference/expectation_propagation_dtc.py,sha256=ZfFh-KeON9jF5Jc0ixeZ6oOnsHos8hBOCHFtfCSZNo4,14034
GPy/inference/latent_function_inference/fitc.py,sha256=tNS14kuQTwH7RKQ6IAjmi1iV3X6wUUyYtPeUHh5mDCc,3137
GPy/inference/latent_function_inference/inferenceX.py,sha256=wfWJKu8J-UVtRo3R-Us3uUQ_D4bfD4blaDk_aSgyd8A,8076
GPy/inference/latent_function_inference/laplace.py,sha256=YSortGXi-uBsZMcm2v2aN19Jy7lnA-QR3_32iIaUJRE,20520
GPy/inference/latent_function_inference/posterior.py,sha256=Djtggv6dTW8idNdYxGzg4Qp8vyVeDkvsG7VfIqANaTs,6888
GPy/inference/latent_function_inference/svgp.py,sha256=oYyPcrWYgUzGWMVseZyYC1E4Q9QpcoKyZwEyqQt_pEE,5144
GPy/inference/latent_function_inference/var_dtc.py,sha256=bjhFl7EcmNGjwmjO1CotnLIe1MVQK4SbamygXUsL6Ig,10582
GPy/inference/latent_function_inference/var_dtc_parallel.py,sha256=rRaWqXJsbWbA6NPU8HuXyyEnVDFcIt78N8wDvF18cYE,18379
GPy/inference/latent_function_inference/var_gauss.py,sha256=Ppwe7R0ohluzL_VI4Gku2zsSKg2cEnLwEq-cOpud3FI,2641
GPy/inference/mcmc/__init__.py,sha256=PujlQkXx_uJNuTHXxkJUg9WGXu2J5h2RA_4mRpFa1us,45
GPy/inference/mcmc/hmc.py,sha256=uP--iR_HLb8b-Vouk8ouLzSB22qb0_xLyqWgOp6vZ7A,7252
GPy/inference/mcmc/samplers.py,sha256=KzNsBna1ZGsIWY14DNJX4kfKz9B32mHTI6TvnHmaYsI,3031
GPy/inference/optimization/__init__.py,sha256=Ss30mt7TschS2Tp61ZUDqGrKeZKjr3-ZFstDhJtyC4I,49
GPy/inference/optimization/conjugate_gradient_descent.py,sha256=SHoRdaQjTp1jpIuMuqj4gTNzWhxbp_z9ysMIuVtmKe8,9983
GPy/inference/optimization/gradient_descent_update_rules.py,sha256=0zazKm7vcCXJHguycL77NVhR8qBEVG8St4EOGvCZaDo,1619
GPy/inference/optimization/optimization.py,sha256=UIl1JoATuSkFlSF50DcS5NACHQGbqfFFXHVVmQZ36hs,8697
GPy/inference/optimization/scg.py,sha256=uVqY6bllInzE3CK-48atP9enbbmMBnn3gxrqEry8kPY,6935
GPy/inference/optimization/stochastics.py,sha256=58AfZI40cvcD510RvKkjO2CPUrQoOZ8BeK9r724MIoE,3334
GPy/kern/__init__.py,sha256=hEv7c2TrK0OqwsfvEf2DVDUI92l4K6vCL6nPSZaOtZQ,1072
GPy/kern/_src/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
GPy/kern/_src/add.py,sha256=cp3WT4sW7hE-EU2PpcoeeF3CJLWcYhqrFtQAKCvmtHs,11331
GPy/kern/_src/basis_funcs.py,sha256=DaYy-NXX3V2uUeV8dilMr6AJccg6u7BE0BKQ1OmVNSc,8243
GPy/kern/_src/brownian.py,sha256=-dgABoXqxK7mARUQmJZUkGx7PilZa4k6t7HYYnu78kg,1735
GPy/kern/_src/coregionalize.py,sha256=BKzpTR50SpnvoD4vAgGtWNhQn8-pv0ActzP0hot-dCs,5126
GPy/kern/_src/coregionalize_cython.c,sha256=gHr4EgL0xGvOCiCTcsMcxUbMs4eBZBtloRIU0MZ6ADk,311890
GPy/kern/_src/coregionalize_cython.pyx,sha256=Fo81nEN9vjkAyyHRvHie_U1-i6juZMTC__XW7WSud3c,1277
GPy/kern/_src/coregionalize_cython.so,sha256=aFRqzR0PXRMfz1Z89S0rYnL8jkeIrEz7iEoMSQ5O-vE,56464
GPy/kern/_src/eq_ode2.py,sha256=dwdXvEDotZjr1AzsBFelNmq0QLeZ293dalkOAXmj8AA,50687
GPy/kern/_src/independent_outputs.py,sha256=ixU1Wze-fMGVzCzM5DXpY-1ExHk3wl1TqK3n4U9HZ2A,10234
GPy/kern/_src/kern.py,sha256=c_Am4CE83l3gvCq1Esg4nTIH1qjmHgBdRue5dSd-mrw,14294
GPy/kern/_src/kernel_slice_operations.py,sha256=cRGABWxVd4xEVBJ8o0savEDGQDV_bCOyTDp7vyYBwts,6236
GPy/kern/_src/linear.py,sha256=T6rtrYbKo0xN2UhIHt4tZqv8Y2gmcZAEGXmrjlSMH3w,7419
GPy/kern/_src/mlp.py,sha256=nfCQcfq2ovODXHEJXcOqz7iuTSPIOHF7GYNesM1mfKw,6658
GPy/kern/_src/ODE_st.py,sha256=0RTpJRI0DPwGWNDrHkXO5SCxAtuLAMMc4OFiWkUxN0Y,12857
GPy/kern/_src/ODE_t.py,sha256=P1fNPPgAtk1EHVG69t2HA5vLi8WCXIv89xzd0yTWPek,7642
GPy/kern/_src/ODE_UY.py,sha256=zkP7QRRtkb9nQCtAitR-eLzsc8rETpzLUjh2w0VImMc,15129
GPy/kern/_src/ODE_UYC.py,sha256=27BzyMpboTJM0z4i5PSAt2C4J8CR6bmnURwDMazLcuk,15562
GPy/kern/_src/periodic.py,sha256=hNdQisVtQUQjZoN4E3ncEOlSEfFABGrOYXyGvlhH9tA,26507
GPy/kern/_src/poly.py,sha256=xuqM311cdhYbHdjeKolajeh6zRi1Q_cdzwP4ZmXYing,1326
GPy/kern/_src/prod.py,sha256=bgLxUERH3GLg7hixBOQH_rYdVkQpFc0aUE1Ht0_ITfo,4019
GPy/kern/_src/rbf.py,sha256=bB2n7bj9R4kIcXJbdWI6HFcrxnOhsSQQdjPeFPPnDcM,3012
GPy/kern/_src/spline.py,sha256=RnjhCsMxkcdtAs4Z6nHiNyEWgnJQI9sd_AnJ80QPvLI,2106
GPy/kern/_src/splitKern.py,sha256=MykjH_qGawYG45MT4B8cwK4_eIvS8UBdevYDVPDUjNI,9042
GPy/kern/_src/standard_periodic.py,sha256=k-cwLMDUgKcAh_P6nXA0YtJJgk-pScZagEZGOOrK6ok,7031
GPy/kern/_src/static.py,sha256=hq2Ki5EVIo4vjy6dCRCLf280cwaSDLHuhpLo0nPO35U,5468
GPy/kern/_src/stationary.py,sha256=q6u79_MoAinohLUwtiSPBa6EWSVh9cVBzwxbWr6wvLg,18736
GPy/kern/_src/stationary_cython.c,sha256=yzQRvoxnQYKyx4rJOP9YCQBoweJ9bAtgx-DcNg6Sxho,848429
GPy/kern/_src/stationary_cython.pyx,sha256=qyfR5ypXF-PTysOmTtg9njlSNgNrC26BK1kNJi8VHAI,2192
GPy/kern/_src/stationary_cython.so,sha256=n-WMFtX-EKDGgG-esjwbt5a-XHr-lxPuZCfqheulZzc,161956
GPy/kern/_src/stationary_utils.c,sha256=HL3EzGVw1hTQ17CC7o5y4VHPrxSZGzYotgI0FnuzEfw,1101
GPy/kern/_src/symbolic.py,sha256=k_EDLaGjsKcgenMnoJnX7H_6DKJAdRRr9LVkpfgF3FY,2868
GPy/kern/_src/trunclinear.py,sha256=fc8JGptGZlSPJ7tt-bKPKVKAVeLeVWlmJKAVkS2H8Ug,7954
GPy/kern/_src/psi_comp/__init__.py,sha256=FVQWHZAn3pxeJwA3hTb7YFqzgs5ZCyGjIHIxlOH8-_0,3565
GPy/kern/_src/psi_comp/gaussherm.py,sha256=z6ZqbCvaNpu2_PAQwHYrv0XjIYDJ2Q3syt7DiM3MNRw,3317
GPy/kern/_src/psi_comp/linear_psi_comp.py,sha256=vT0wOoQDi0svld4QifVWhlaPm2AcFG3CXkMqSYEg-YI,3548
GPy/kern/_src/psi_comp/rbf_psi_comp.py,sha256=o_Wga9TN8H5_ZsfXhixQcWEecR4SUQUaHoeyAk6D1NA,5366
GPy/kern/_src/psi_comp/rbf_psi_gpucomp.py,sha256=S4PXiYtqfAc1C1NXo3uKOlcQQvI0ctFlfE-yzfqN27M,20171
GPy/kern/_src/psi_comp/sslinear_psi_comp.py,sha256=pdrYh73FbIC5m8wz7n3ReMl0WqC350GK7sxd0k8J-gY,2983
GPy/kern/_src/psi_comp/ssrbf_psi_comp.py,sha256=RM3IWcMlqbVVxpKMyW21EoGGkfm3QGxW9hOMgW8U63c,18839
GPy/kern/_src/psi_comp/ssrbf_psi_gpucomp.py,sha256=72PWBVH9-0ajOXhHojDKR77S9Dod91rl2uXKF2ZXrEM,24403
GPy/kern/_src/todo/odekern1.c,sha256=n1SAUTU7qoK5wWsJDAyJ6ScjIJunjpF4CA1JMy385PM,503
GPy/likelihoods/__init__.py,sha256=kUMk4FqDZED_fv5K7coshFmEY6jYpAWlDRNe92Wxjkg,315
GPy/likelihoods/bernoulli.py,sha256=lTLx-hzz9gvff_262PWR5qVSvhpCHnf-DJqbj2n3KWM,10108
GPy/likelihoods/binomial.py,sha256=hw208Bw_HHzzhpsuPj8K_NjkCpFYIyTod-hku2BaMtg,4599
GPy/likelihoods/exponential.py,sha256=VOD2wkpvB0KcFihO1rJ1j0dZIy5BiO4qd8I_tcVOGd4,4727
GPy/likelihoods/gamma.py,sha256=sh_CZfxHCkqCqqQ4-t3DijNqe0qN4wRdHUMAgIft90E,5833
GPy/likelihoods/gaussian.py,sha256=hWz06wcn__obKMy6B5B_RbmG4m3OaqHQhMz1glGw6gw,14009
GPy/likelihoods/likelihood.py,sha256=q4EuZVRibykDvwVpmMaiouzKGONbvRlEB6GZ2WduJiM,30737
GPy/likelihoods/link_functions.py,sha256=I_QQaSZMFbE9-wVW3bbTOfnIvsAcGMRiyb_r6FsSzlE,4019
GPy/likelihoods/mixed_noise.py,sha256=o2UMR_5wr4zNUAFxZ3oAye-1K7QgIl7zrUoIK26TtFw,3283
GPy/likelihoods/poisson.py,sha256=ZQerGUKLchvUnxIWt7yy4ST0mvHGHYQ81bTqJ1DvZJk,5231
GPy/likelihoods/student_t.py,sha256=KG1KkPROJq0NNpPIw1YPi2J-qECEeBv-AnzZpjGAGMI,13101
GPy/mappings/__init__.py,sha256=dvJsO3RFAqydeubxjvXkt9oHKUbuyXPM82BBrP7_hYI,288
GPy/mappings/additive.py,sha256=K3XmQfxAbx5jEzSbnxR3CIj8bSDVOl9coVSCGvWFA0E,1241
GPy/mappings/compound.py,sha256=wbKx9VLzNMcBPrl3xxOCaBUZvrdP7YHzr66RdxiChpU,1274
GPy/mappings/constant.py,sha256=2lOlyvkFvrN-oGBPUpE7zHofRCwt2wTquNu6WDjwnE0,1144
GPy/mappings/identity.py,sha256=V4TDyWp4LlnM8DpELKUJAToBghQyKxrHFaQLPzDkjzw,455
GPy/mappings/kernel.py,sha256=bPV8EqEjkWE855_lQcEwc80Z-rygoPjgTUwqo24_bys,1602
GPy/mappings/linear.py,sha256=sdlIoxvanV7QfJn86EBJ1HP2cCHgen-v-5aP_hzPK9E,1088
GPy/mappings/mlp.py,sha256=Pox1ZMTq0v78zgY3PFa7W8-x3uFcSRl-rwrlc8cElqE,1931
GPy/mappings/piecewise_linear.py,sha256=zAN53MRoehg7sKCjHfpBs5wYKA57z3hFnlUEf3NTZbs,3707
GPy/models/__init__.py,sha256=J_FKllwnXeGjiJtZ0HvAZYV3FDQXlLYk3AFsaxGZ5gQ,1201
GPy/models/bayesian_gplvm.py,sha256=Tft8K9d2REQUWSueqftCCfwbiGA0bPtsZ-zL0Zu59fI,10126
GPy/models/bayesian_gplvm_minibatch.py,sha256=L_9NP2AOv9WCg1L_P5HTTat2K9iJMSZQ6pGxU3dKh8Q,10878
GPy/models/bcgplvm.py,sha256=Ixk3wIU1yFje0Y7-Gwz_S06LtvOJdzfrbZWl_syeDrM,1466
GPy/models/dpgplvm.py,sha256=2FwN5zj5rftVWoMh8L0E7hfC5dF-mJ642Ci2LmpQFWQ,1162
GPy/models/gp_classification.py,sha256=kbJyLJlVToDUa_gAjZLa5C3iucxGGgZiTS0ElXcIScU,960
GPy/models/gp_coregionalized_regression.py,sha256=-UBhLGCqrFSv7kKr_zGR0uXFqnmeW6E_qYOrd0Xyun4,1876
GPy/models/gp_heteroscedastic_regression.py,sha256=_yTqm8xEaaIP6DYHQiZWzh7HcOTQo3y5_a1sJEx4axQ,1212
GPy/models/gp_kronecker_gaussian_regression.py,sha256=8HB9RGO6_8TEu5rYv1TChuGyyKeRvNVzMXT-eM1rz8Y,4383
GPy/models/gp_regression.py,sha256=8Cm7xZVt8WcDSWm-oWF4UQ3KO6RejNYxW-FQuygUXrU,1259
GPy/models/gp_var_gauss.py,sha256=1sZEHELKw1N21zjgSQbTtTTxvewlkc_hq3FUK9S3kaE,1207
GPy/models/gplvm.py,sha256=aLqF-dFHXzHQlYbKDfyHdvCTvAwlWt2tEe9OploWeXA,3049
GPy/models/gradient_checker.py,sha256=R35SuOjlju9nvqh_OS0A7JoFAJd6r_ogmBjVHG0brPE,17212
GPy/models/mrd.py,sha256=yOzBruCKE6nx9xpqLQE0He9iTZPc5J_vFzn6sfuvW2Q,14617
GPy/models/one_vs_all_classification.py,sha256=jDQmOj1v-QGKnTUZlz29-rjZi3aq6zTybsdN0wikgoc,1374
GPy/models/one_vs_all_sparse_classification.py,sha256=GmUKLMLLuClEufaGgY6ucD5w8rVUCNKSTT4Vnw8dH4A,1399
GPy/models/sparse_gp_classification.py,sha256=928bt70rUr6Pluf9bzNWAGoZE_TdAurVoexSrPm7HFQ,1855
GPy/models/sparse_gp_coregionalized_regression.py,sha256=7f-p12EY1vw8lMKFCERX5qaKl2eMO1BApR3iJwS6N5w,2992
GPy/models/sparse_gp_minibatch.py,sha256=QLVAW4W3cCeQCTYSBe-PvC5DQTWsEmC_Z2lQmaAC2Po,15338
GPy/models/sparse_gp_regression.py,sha256=ebImD6IEXBqAEOGwTXPZ4f8qx7Ng0AaINYRQ5Jpyl2s,4492
GPy/models/sparse_gplvm.py,sha256=wfiL6gtqA8XfIVX1ViL5EBTYWMASkEP-a6li_pD74TA,1818
GPy/models/ss_gplvm.py,sha256=ATPsQ46_9VND7QtpCDDHTHYHiswZ3t9Yg-Cwk3GAiIw,9690
GPy/models/ss_mrd.py,sha256=X9wflmqVE0s1pRvd_7Up7Ws4L9maHUDGZ_p4f7Op0Gs,12960
GPy/models/warped_gp.py,sha256=TDUXf4hyhlmW5ty-TlIsFibpBkWLVeHXJux-07xRiYI,3327
GPy/plotting/__init__.py,sha256=YR51Hf0xgVCuwwjcwhMaH7qQM8TW2esR3anXmhadx18,514
GPy/plotting/matplot_dep/__init__.py,sha256=e28hmWwbDz95wcD9ZQt1bWwQwWk8FycL2qpkRkqJ-HA,435
GPy/plotting/matplot_dep/base_plots.py,sha256=AhxjnEpXMSCSeA5yrijuUTysgfu65ssYwoxMm-SH-Ts,5445
GPy/plotting/matplot_dep/dim_reduction_plots.py,sha256=8vfjXSCh7vQfToAl0J0fVX-1MFtbdwBG-Z7SmopSn64,16751
GPy/plotting/matplot_dep/img_plots.py,sha256=zcCRBxx669M6muI8NV1EU30ud4aZbb2D5FRZUhJCAuk,2159
GPy/plotting/matplot_dep/inference_plots.py,sha256=WN0b67d9kvy0G8qvl0PbmUH397fyjBkiCzbaqFa_zEQ,863
GPy/plotting/matplot_dep/kernel_plots.py,sha256=XG7FU9DB5djHwakg5FetlhiH8qJf0zVUPI3mxFxl68I,6216
GPy/plotting/matplot_dep/mapping_plots.py,sha256=LQi8PzInFITB-xXy980UZP7Memq2GAVumfiS38WD0-c,3485
GPy/plotting/matplot_dep/maps.py,sha256=7LU-eqE1SW-THc_vWGjS9ScoTRaBMvcqkdidsgxSKrg,5721
GPy/plotting/matplot_dep/models_plots.py,sha256=ZT9LyxbdTDMWC6WglIy7Dj2WdSf3sykmRpApPuyohpw,18909
GPy/plotting/matplot_dep/netpbmfile.py,sha256=Aftt984BAu_E5U8-9khJJat7vAdqUtV-Y9rEU3-E1DE,12223
GPy/plotting/matplot_dep/priors_plots.py,sha256=ToMH0p45TTwlG3YVnNhkwC2ZwsgeXXKoeA6ofovceQc,909
GPy/plotting/matplot_dep/ssgplvm.py,sha256=Ud-g0ly8egQZdnMAbMevAswSuvgt48CQqVxfaQqHP50,829
GPy/plotting/matplot_dep/svig_plots.py,sha256=4xAJCKmmOhPVs5O-xIrs5kJKuCo7Z1q0VqguVwDe9Rw,1323
GPy/plotting/matplot_dep/Tango.py,sha256=7b4JK5KNt_OE8-voNoHWGkRrQ25l1o5uBemYwXPk8BA,8127
GPy/plotting/matplot_dep/variational_plots.py,sha256=E6PgkIG4vMZeZvJ6UPtwklsZLsUCl45qT_2YjwBk8TA,3973
GPy/plotting/matplot_dep/visualize.py,sha256=uqfd2aD1RAq-9PuUVrVH6XF0JOjxeHA8cMUaSrIW4x0,23219
GPy/plotting/matplot_dep/latent_space_visualizations/__init__.py,sha256=U9X8-XX1Rxf3tExcM-tCq52ICRIlEs7eVMRkPxNG_b4,19
GPy/plotting/matplot_dep/latent_space_visualizations/controllers/__init__.py,sha256=kMaqSDmRKZ0npYvHTPHi5seFhIel0xQC0I9rUNncEA0,48
GPy/plotting/matplot_dep/latent_space_visualizations/controllers/axis_event_controller.py,sha256=WBv3hfG-Mi31dYhCmsEaiTsvm4O2S6O-Sp_IikmPtpU,5733
GPy/plotting/matplot_dep/latent_space_visualizations/controllers/imshow_controller.py,sha256=fP-Bl9YpgBYcwoEdqQsP6Fup_xhM14oVTDC--AkirQs,3393
GPy/testing/__init__.py,sha256=UjyWtG0LpPXScWxQ5Jm3OrO5VdszhJair-dnACKhZBI,291
GPy/testing/bgplvm_minibatch_tests.py,sha256=o4XQATGcjj4BTrE7q3ObUpF3r-4q19GGRnfpV9vv7fM,5759
GPy/testing/cacher_tests.py,sha256=uoIPGk8hKXWLQV8844erw-Fd_Kv7hUJmeW5IRoCGWEE,1022
GPy/testing/cython_tests.py,sha256=r5VAPbmCUd6_KN3-XkveMcuFOIQHQH57ZCjSaIc09MM,3000
GPy/testing/examples_tests.py,sha256=aqM-2G5Vm_eh0s4VB-7ECylt15n_Rm2C8X98TCAuZ3Q,3721
GPy/testing/fitc.py,sha256=3KX-uTMhDpL9qT6hUMhjv52tNWj71urTKx3lWR2CwfI,1138
GPy/testing/gp_tests.py,sha256=ifFT80MGxxbbtXF4L8rKEo701nvzrvtiG_8agQB_YcE,3381
GPy/testing/index_operations_tests.py,sha256=hSEUx6QMW5ZX7wyEsddRKmi43wvlWPRTDisNsCo6cK4,6775
GPy/testing/inference_tests.py,sha256=6sikKWNFGVZg6g01H50AHOMVUGkH0Xj6mSZpsxZWowQ,2300
GPy/testing/kernel_tests.py,sha256=cyoKZqAlhKVr6-BhuyzS0pWIuxBYPIvX_HaLLqM4ESQ,22701
GPy/testing/likelihood_tests.py,sha256=2FoKJAFlXILFBgPOams4cIyLjD-bPg5311M1VzE_LjE,35323
GPy/testing/linalg_test.py,sha256=fMMZFkI7ZiWo5wZYCgoo0XMW5G7lxoeJS1_j5Yh9La0,2166
GPy/testing/link_function_tests.py,sha256=xTtiVf0fZ3T1m070k_jdGG2N3KlTyDM5FlKQwXyPshQ,6783
GPy/testing/mapping_tests.py,sha256=u_gHnQ06KK8ndiq7N-RwYKqGR7fd226lB5zFHonPgug,2518
GPy/testing/meanfunc_tests.py,sha256=xVuz1QT_Vs2bCRWetca8xAHT-Gvphqih6Mq6Ik_DAYY,1846
GPy/testing/misc_tests.py,sha256=b82QpdqwxvNTHIonsnAN6XMTbEBeW2jxwSNfPRqdBFw,862
GPy/testing/model_tests.py,sha256=vRKtQXDl1O5P6alzytEty6pa23my0880LPOkR-5OIYw,25915
GPy/testing/mpi_tests.py,sha256=KMoyfQArJp_9iS8mwX5CbILgTNGDneXnT2xJxLbMEvM,2682
GPy/testing/observable_tests.py,sha256=n0yjhtnllvcKAVg6Sl7cBEM_seJ0tzqoI_K_XkmAahQ,6069
GPy/testing/parameterized_tests.py,sha256=D9AFL2R7_sBsfMzDQ_nbkhJWo7da-Dc_m8Xrgq8ENtg,12185
GPy/testing/pickle_tests.py,sha256=VfoPOW_WRWDvzM3MpvgXmw77HtzDB4WYQr8_jLSSLdM,9315
GPy/testing/prior_tests.py,sha256=bzADI98U9aPEbBKOlXtksfwdwcNuZt3xAtav4stRyUI,3890
GPy/testing/rv_transformation_tests.py,sha256=0gRlMwvy5HoiGrbm-3EfbOAydOX43PaiMHq2TuUW3aQ,3522
GPy/testing/svgp_tests.py,sha256=ghfzeyLIo11-Vm7TGnmkG1iDzeh1xPXJV3kQxnoop-Q,1851
GPy/util/__init__.py,sha256=Snl-JTVCxo8i4tay--tjwuabrZfSvuyOKwQYoYauxoo,463
GPy/util/block_matrices.py,sha256=MI7F0qhQAYGoJZBFqPwIgHxlrjk3MrRRNJlCoR0aDrs,4522
GPy/util/caching.py,sha256=UuMsvUFH-JrizXv0Wu4txo55ao0DAmYjqkWaRYqSptY,8978
GPy/util/choleskies.py,sha256=_Ldjfk8MWWxGjjGopC8cPEnTMzCziiGgv2QiqhO5pvs,3276
GPy/util/choleskies_cython.c,sha256=2tc0a-8WYPXkq8Y-JUWXXtpljyG9y-OAH4StOrHXOTw,828046
GPy/util/choleskies_cython.pyx,sha256=nDLIT7rsymI0x0nqh92P75qfQE_R78yw11g0zg9NmIQ,3706
GPy/util/choleskies_cython.so,sha256=_mNfB4XTM1WiIqKxfI-9dD7eRMbwbwrcp5opULA_52E,171084
GPy/util/classification.py,sha256=IdczXf4K9Hsa5aR52-eujPvGwjGSi2CQH_tdsFxNpB8,1489
GPy/util/config.py,sha256=R4skIkGhnao3la5tQbzUYLJ2y0xGk1aynku4jrzmf8g,1147
GPy/util/data_resources.json,sha256=eLrtnnv07Z0wKFUUh8tDKs_ux5I3ZPNbhHvIiQWnRq0,34017
GPy/util/datasets.py,sha256=l7aN7QYi-CeZhEiS7oqyPamv3DmDVLOgvtk_aFHzars,64882
GPy/util/debug.py,sha256=K3Ti2ITgMHgNDzaSkUuMF-QPfY9RxHMCfVJ1TxtJsA8,1108
GPy/util/decorators.py,sha256=r76CTYkWA6cXfw7PFKMFTregLE4H0o-fUwI7hL-cqFw,566
GPy/util/diag.py,sha256=HdcvAyCZxtqg7x4R0xGuoGAsqSroYaEGT9pi5C7qe4k,3853
GPy/util/football_teams.json,sha256=gA_kLJOO0eHaMPeFhG2o5sWfcs_gEifV2DWXA0ceyIs,2429
GPy/util/functions.py,sha256=c7V_FeJnbUvHnnS-6vLGPPjnwsaQTSJrYyA5NeKVbks,1110
GPy/util/gpu_init.py,sha256=t0TXdzXcb9WeeZzEst-WwtsS9mvFXePWbNeIbakuRJ8,1012
GPy/util/initialization.py,sha256=9l3xx7NnGLINK0Nin1ZN82vK21uMmuiwJ-snE29BXmc,488
GPy/util/linalg.py,sha256=f5EyNR7VYMRC_Et__qwfAh8XKADzfbYSVlJj1ZZXHgg,11666
GPy/util/linalg_cython.c,sha256=UVgF3Mi9nMwXQYecq5Ve82659i0xfRmoLzjYmvO7ucs,274362
GPy/util/linalg_cython.pyx,sha256=fVywVo3Yyu7kfj-II-JG30AlssTxII4lxgvbaSsX9dI,1038
GPy/util/linalg_cython.so,sha256=Rpmd1yDMiq6FSPUCTRStUR1of86UgUAIdJAeltmj1d8,41036
GPy/util/linalg_gpu.py,sha256=4K9IY1vtgsHqXdUbCi3ub4rW7VjjXXUwKN7pdBvnfOM,3038
GPy/util/ln_diff_erfs.py,sha256=l8LjCPbGPdEChl-fPUHq9goJUpBwaibOhbuKnHO5R7g,3554
GPy/util/misc.py,sha256=Mj4KQY-JvBwaZIxmYHxeKZsgAyRVwTSWEs35dxn1wJ0,5919
GPy/util/mocap.py,sha256=f9DLG_jwIUIwZ8IkAbBj6JoL8wyoGWwphLMA8PkUWck,27243
GPy/util/multioutput.py,sha256=AUMx1mTTG7vpthLwK8eCwVrBqhNM41qGe6eVW3yUqb4,3588
GPy/util/netpbmfile.py,sha256=Aftt984BAu_E5U8-9khJJat7vAdqUtV-Y9rEU3-E1DE,12223
GPy/util/normalizer.py,sha256=KgGTrnqw9wtAXBjWORHnxhl_7hW3Pbdgp6w5WePtvBU,1116
GPy/util/parallel.py,sha256=Cknc51TgfpM4dQu_DAtjLvM8eJ2rFoounGdXUEGW_Ec,2081
GPy/util/pca.py,sha256=QQyMA5qO2Fl3XUAfQvCxQvpSLwbBB37Yfk7PiqeFDO0,4762
GPy/util/squashers.py,sha256=MhSYJLY1FeZNdTUEmjRpzK8eSmTcq4ghWBlSVi6UtJk,340
GPy/util/subarray_and_sorting.py,sha256=GA35AA_1YR-aKHsAf_1zjxEkuqRsbNfa1U-PCvrFw1k,1961
GPy/util/univariate_Gaussian.py,sha256=hnRG2WUvzvS2i9SruhUyunw-MREAqaU5HWBEseO6Flw,706
GPy/util/warping_functions.py,sha256=coRMWn5qESGbqA7679HIhqMAAqInJDX-42g4KPVSJdI,8459
GPy-0.8.8.dist-info/DESCRIPTION.rst,sha256=fju029Pp1eGAwhcqrsHZZzcpxytpX29QB-4G5Nn2oLM,8335
GPy-0.8.8.dist-info/METADATA,sha256=QniqbvOB04B1JPx6wKF4OjUt55NmHO7Xfcid3N8NxRM,8722
GPy-0.8.8.dist-info/metadata.json,sha256=LCxumdOhxIP8ZRzmJK947fVt6bZq0pOOHSxDanmlql4,531
GPy-0.8.8.dist-info/RECORD,,
GPy-0.8.8.dist-info/top_level.txt,sha256=3WnOTLxgkLtrALq5mDm_ncvSTr4No2ZG8bY2k2eGark,4
GPy-0.8.8.dist-info/WHEEL,sha256=01eFPJ2ZUbdI4vqzopt-j-dPpvVtrqAzRoGkGNpzaik,110
