remove progress bar

Author: amal-ghamdi

chapter03/basics_bayesian_inverse_problems.ipynb

@@ -78,7 +78,8 @@
     "from cuqi.testproblem import Deconvolution1D, Deconvolution2D\n",
     "from cuqi.distribution import JointDistribution, Gaussian, CMRF, LMRF, GMRF\n",
     "from cuqi.experimental.mcmc import LinearRTO, PCN, CWMH, ULA, MALA, NUTS\n",
-    "from cuqi.problem import BayesianProblem\n"
+    "from cuqi.problem import BayesianProblem\n",
+    "cuqi.config.DISABLE_PROGRESS_BAR = True\n"
    ]
   },
   {

chapter04/gibbs.ipynb

@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -68,7 +68,8 @@
     "from cuqi.testproblem import Deconvolution1D\n",
     "from cuqi.distribution import Gaussian, Gamma, JointDistribution, GMRF, LMRF\n",
     "from cuqi.experimental.mcmc import HybridGibbs, LinearRTO, Conjugate, UGLA, ConjugateApprox, Direct\n",
-    "from cuqi.problem import BayesianProblem"
+    "from cuqi.problem import BayesianProblem\n",
+    "cuqi.config.DISABLE_PROGRESS_BAR = True"
    ]
   },
   {

chapter07/more_theory_on_sampling_with_cuqipy.ipynb

@@ -56,10 +56,11 @@
     "import inspect\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "from cuqi.sampler import MH, CWMH, ULA, MALA, NUTS\n",
+    "from cuqi.experimental.mcmc import MH, CWMH, ULA, MALA, NUTS\n",
     "import time\n",
     "import scipy.stats as sps\n",
-    "from scipy.stats import gaussian_kde"
+    "from scipy.stats import gaussian_kde\n",
+    "cuqi.config.DISABLE_PROGRESS_BAR = True"
    ]
   },
   {
@@ -256,7 +257,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "MH_sampler = MH(target_donut, scale=scale, x0=np.array([0,0]))"
+    "MH_sampler = MH(target_donut, scale=scale, initial_point=np.array([0,0]))"
    ]
   },
   {
@@ -278,7 +279,8 @@
    },
    "outputs": [],
    "source": [
-    "MH_fixed_samples = MH_sampler.sample(Ns, Nb)"
+    "MH_sampler.sample(Ns+Nb)\n",
+    "MH_fixed_samples = MH_sampler.get_samples().burnthin(Nb)"
    ]
   },
   {
@@ -297,6 +299,7 @@
    "outputs": [],
    "source": [
     "plot_pdf_2D(target_donut, -4, 4, -4, 4)\n",
+    "\n",
     "MH_fixed_samples.plot_pair(ax=plt.gca())"
    ]
   },
@@ -370,7 +373,9 @@
    "source": [
     "Ns = 8500\n",
     "Nb = 1500\n",
-    "MH_adapted_samples = MH_sampler.sample_adapt(Ns, Nb)"
+    "MH_sampler.warmup(Nb)\n",
+    "MH_sampler.sample(Ns)\n",
+    "MH_adapted_samples = MH_sampler.get_samples().burnthin(Nb)"
    ]
   },
   {
@@ -523,8 +528,10 @@
    },
    "outputs": [],
    "source": [
-    "MH_sampler = MH(target_poisson, scale = scale, x0=np.ones(target_poisson.dim))\n",
-    "MH_samples = MH_sampler.sample_adapt(Ns, Nb)"
+    "MH_sampler = MH(target_poisson, scale = scale, initial_point=np.ones(target_poisson.dim))\n",
+    "MH_sampler.warmup(Nb)\n",
+    "MH_sampler.sample(Ns)\n",
+    "MH_samples = MH_sampler.get_samples().burnthin(Nb)"
    ]
   },
   {
@@ -546,8 +553,10 @@
    },
    "outputs": [],
    "source": [
-    "CWMH_sampler = CWMH(target_poisson, scale = scale, x0=np.ones(target_poisson.dim))\n",
-    "CWMH_samples = CWMH_sampler.sample_adapt(Ns, Nb)"
+    "CWMH_sampler = CWMH(target_poisson, scale = scale, initial_point=np.ones(target_poisson.dim))\n",
+    "CWMH_sampler.warmup(Nb)\n",
+    "CWMH_sampler.sample(Ns)\n",
+    "CWMH_samples = CWMH_sampler.get_samples().burnthin(Nb)\n"
    ]
   },
   {
@@ -667,7 +676,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ULA_sampler = ULA(target=target_donut, scale=0.065, x0=np.array([0,0]))"
+    "ULA_sampler = ULA(target=target_donut, scale=0.065, initial_point=np.array([0,0]))"
    ]
   },
   {
@@ -690,7 +699,8 @@
    "outputs": [],
    "source": [
     "Ns = 1000\n",
-    "ULA_samples = ULA_sampler.sample(Ns)"
+    "ULA_sampler.sample(Ns)\n",
+    "ULA_samples = ULA_sampler.get_samples()"
    ]
   },
   {
@@ -828,10 +838,11 @@
    },
    "outputs": [],
    "source": [
-    "MALA_uni = MALA(x_uni, scale=1, x0=0)\n",
+    "MALA_uni = MALA(x_uni, scale=1, initial_point=0)\n",
     "\n",
     "Ns = 40000\n",
-    "ULA_samples_uni = MALA_uni.sample(Ns)"
+    "MALA_uni.sample(Ns)\n",
+    "ULA_samples_uni = MALA_uni.get_samples()"
    ]
   },
   {
@@ -978,7 +989,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "NUTS_donut = NUTS(target=target_donut, x0=np.array([0,0]))"
+    "NUTS_donut = NUTS(target=target_donut, initial_point=np.array([0,0]))"
    ]
   },
   {
@@ -1002,7 +1013,9 @@
    "source": [
     "Ns = 100\n",
     "Nb = 10\n",
-    "NUTS_donuts_samples = NUTS_donut.sample(Ns, Nb)"
+    "NUTS_donut.warmup(Nb)\n",
+    "NUTS_donut.sample(Ns)\n",
+    "NUTS_donuts_samples = NUTS_donut.get_samples().burnthin(Nb)"
    ]
   },
   {
@@ -1098,7 +1111,7 @@
    "notebook_metadata_filter": "-all"
   },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "fenicsproject",
    "language": "python",
    "name": "python3"
   },