Deploying to gh-pages from @ 06e0a15 🚀

brainets · Sep 3, 2024 · dacf1f5 · dacf1f5
1 parent 9b3fd0c
commit dacf1f5
Show file tree

Hide file tree

Showing 116 changed files with 896 additions and 157 deletions.
diff --git a/_downloads/071683bb9f5d321841ee9dd62eee27a4/plot_sim_red_syn.zip b/_downloads/071683bb9f5d321841ee9dd62eee27a4/plot_sim_red_syn.zip
diff --git a/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip b/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip
diff --git a/_downloads/17dd9833b54c0c0905f696d14e67b05d/plot_bootstrapping.zip b/_downloads/17dd9833b54c0c0905f696d14e67b05d/plot_bootstrapping.zip
diff --git a/_downloads/1e58f0eaeee8456990c2959bfc1babe9/plot_oinfo.zip b/_downloads/1e58f0eaeee8456990c2959bfc1babe9/plot_oinfo.zip
diff --git a/_downloads/31d2b25c7ef60a5789246c25d83787c6/plot_ml_vs_it.zip b/_downloads/31d2b25c7ef60a5789246c25d83787c6/plot_ml_vs_it.zip
diff --git a/_downloads/34da59110fbcc6d2797595ef80bd7328/plot_inspect_results.zip b/_downloads/34da59110fbcc6d2797595ef80bd7328/plot_inspect_results.zip
diff --git a/_downloads/4481fca80fcd36eb6fde9d9cb4e3134c/plot_entropies.py b/_downloads/4481fca80fcd36eb6fde9d9cb4e3134c/plot_entropies.py
@@ -14,6 +14,7 @@
 """
 
 # %%
+
 import numpy as np
 
 from hoi.core import get_entropy
@@ -34,7 +35,7 @@
 # list of estimators to compare
 metrics = {
     "GC": get_entropy("gc"),
-    "Gaussian": get_entropy("gauss"),
+    "Gaussian": get_entropy(method="gauss"),
     "KNN-3": get_entropy("knn", k=3),
     "Kernel": get_entropy("kernel"),
 }

diff --git a/_downloads/4edd620b8310fd78193d26c454d1ca13/plot_rsi.zip b/_downloads/4edd620b8310fd78193d26c454d1ca13/plot_rsi.zip
diff --git a/_downloads/58edfc8265b6af8f03b242ea4656b10f/plot_syn_phiID.zip b/_downloads/58edfc8265b6af8f03b242ea4656b10f/plot_syn_phiID.zip
diff --git a/_downloads/675ecb045af78b81dbd01cbea1d59fa1/plot_entropies_mvar.zip b/_downloads/675ecb045af78b81dbd01cbea1d59fa1/plot_entropies_mvar.zip
diff --git a/_downloads/69a8436c098192f96580d8e9cd2f5d4d/plot_entropies.zip b/_downloads/69a8436c098192f96580d8e9cd2f5d4d/plot_entropies.zip
diff --git a/_downloads/6df867479300c4bb75728e18f2fcc139/plot_infotopo.zip b/_downloads/6df867479300c4bb75728e18f2fcc139/plot_infotopo.zip
diff --git a/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip b/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip
diff --git a/_downloads/7aa0a86e987814b09694d9eac79452ec/plot_mi.py b/_downloads/7aa0a86e987814b09694d9eac79452ec/plot_mi.py
@@ -13,6 +13,8 @@
 4. See if the computed MI converge toward the theoretical value
 """
 
+# %%
+
 import numpy as np
 from functools import partial
 
@@ -35,10 +37,10 @@
 # create a special function for the binning approach as it requires binary data
 mi_binning_fcn = get_mi("binning", base=2)
 
-
 def mi_binning(x, y, **kwargs):
     x = digitize(x.T, **kwargs).T
     y = digitize(y.T, **kwargs).T
+
     return mi_binning_fcn(x, y)
 
 

diff --git a/_downloads/88e7424e21f0645541ebb2bb32c0cfda/plot_mi_high_dimensional.zip b/_downloads/88e7424e21f0645541ebb2bb32c0cfda/plot_mi_high_dimensional.zip
diff --git a/_downloads/9b8182d6a0423d2c5a54190326bb52f0/plot_jax.zip b/_downloads/9b8182d6a0423d2c5a54190326bb52f0/plot_jax.zip
diff --git a/_downloads/a1ef8dbbc19c785bec2a5cc12335798e/plot_entropies.ipynb b/_downloads/a1ef8dbbc19c785bec2a5cc12335798e/plot_entropies.ipynb
@@ -33,7 +33,7 @@
       },
       "outputs": [],
       "source": [
-        "# list of estimators to compare\nmetrics = {\n    \"GC\": get_entropy(\"gc\"),\n    \"Gaussian\": get_entropy(\"gauss\"),\n    \"KNN-3\": get_entropy(\"knn\", k=3),\n    \"Kernel\": get_entropy(\"kernel\"),\n}\n\n# number of samples to simulate data\nn_samples = np.geomspace(20, 1000, 15).astype(int)\n\n# number of repetitions to estimate the percentile interval\nn_repeat = 10\n\n\n# plotting function\ndef plot(h_x, h_theoric):\n    \"\"\"Plotting function.\"\"\"\n    for n_m, metric_name in enumerate(h_x.keys()):\n        # get the entropies\n        x = h_x[metric_name]\n\n        # get the color\n        color = f\"C{n_m}\"\n\n        # estimate lower and upper bounds of the [5, 95]th percentile interval\n        x_low, x_high = np.percentile(x, [5, 95], axis=0)\n\n        # plot the entropy as a function of the number of samples and interval\n        plt.plot(n_samples, x.mean(0), color=color, lw=2, label=metric_name)\n        plt.fill_between(n_samples, x_low, x_high, color=color, alpha=0.2)\n\n    # plot the theoretical value\n    plt.axhline(\n        h_theoric, linestyle=\"--\", color=\"k\", label=\"Theoretical entropy\"\n    )\n    plt.legend()\n    plt.xlabel(\"Number of samples\")\n    plt.ylabel(\"Entropy [bits]\")"
+        "# list of estimators to compare\nmetrics = {\n    \"GC\": get_entropy(\"gc\"),\n    \"Gaussian\": get_entropy(method=\"gauss\"),\n    \"KNN-3\": get_entropy(\"knn\", k=3),\n    \"Kernel\": get_entropy(\"kernel\"),\n}\n\n# number of samples to simulate data\nn_samples = np.geomspace(20, 1000, 15).astype(int)\n\n# number of repetitions to estimate the percentile interval\nn_repeat = 10\n\n\n# plotting function\ndef plot(h_x, h_theoric):\n    \"\"\"Plotting function.\"\"\"\n    for n_m, metric_name in enumerate(h_x.keys()):\n        # get the entropies\n        x = h_x[metric_name]\n\n        # get the color\n        color = f\"C{n_m}\"\n\n        # estimate lower and upper bounds of the [5, 95]th percentile interval\n        x_low, x_high = np.percentile(x, [5, 95], axis=0)\n\n        # plot the entropy as a function of the number of samples and interval\n        plt.plot(n_samples, x.mean(0), color=color, lw=2, label=metric_name)\n        plt.fill_between(n_samples, x_low, x_high, color=color, alpha=0.2)\n\n    # plot the theoretical value\n    plt.axhline(\n        h_theoric, linestyle=\"--\", color=\"k\", label=\"Theoretical entropy\"\n    )\n    plt.legend()\n    plt.xlabel(\"Number of samples\")\n    plt.ylabel(\"Entropy [bits]\")"
       ]
     },
     {

diff --git a/_downloads/b4b212cbccfe9b262b7e665ad86bc92a/plot_tutorial_core.zip b/_downloads/b4b212cbccfe9b262b7e665ad86bc92a/plot_tutorial_core.zip
diff --git a/_downloads/cb54325e1a49091788b29a83388e5628/plot_mi.zip b/_downloads/cb54325e1a49091788b29a83388e5628/plot_mi.zip
diff --git a/_downloads/f14e92090ebeb63084124f7809315b90/plot_mi.ipynb b/_downloads/f14e92090ebeb63084124f7809315b90/plot_mi.ipynb
@@ -33,7 +33,7 @@
       },
       "outputs": [],
       "source": [
-        "# create a special function for the binning approach as it requires binary data\nmi_binning_fcn = get_mi(\"binning\", base=2)\n\n\ndef mi_binning(x, y, **kwargs):\n    x = digitize(x.T, **kwargs).T\n    y = digitize(y.T, **kwargs).T\n    return mi_binning_fcn(x, y)\n\n\n# list of estimators to compare\nmetrics = {\n    \"GC\": get_mi(\"gc\", biascorrect=False),\n    \"KNN-3\": get_mi(\"knn\", k=3),\n    \"KNN-10\": get_mi(\"knn\", k=10),\n    \"Kernel\": get_mi(\"kernel\"),\n    \"Binning\": partial(mi_binning, n_bins=4),\n}\n\n# number of samples to simulate data\nn_samples = np.geomspace(20, 1000, 10).astype(int)\n\n# number of repetitions to estimate the percentile interval\nn_repeat = 10\n\n\n# plotting function\ndef plot(mi, mi_theoric):\n    \"\"\"Plotting function.\"\"\"\n    for n_m, metric_name in enumerate(mi.keys()):\n        # get the entropies\n        x = mi[metric_name]\n\n        # get the color\n        color = f\"C{n_m}\"\n\n        # estimate lower and upper bounds of the [5, 95]th percentile interval\n        x_low, x_high = np.percentile(x, [5, 95], axis=0)\n\n        # plot the MI as a function of the number of samples and interval\n        plt.plot(n_samples, x.mean(0), color=color, lw=2, label=metric_name)\n        plt.fill_between(n_samples, x_low, x_high, color=color, alpha=0.2)\n\n    # plot the theoretical value\n    plt.axhline(mi_theoric, linestyle=\"--\", color=\"k\", label=\"Theoretical MI\")\n    plt.legend()\n    plt.xlabel(\"Number of samples\")\n    plt.ylabel(\"Mutual-information [bits]\")"
+        "# create a special function for the binning approach as it requires binary data\nmi_binning_fcn = get_mi(\"binning\", base=2)\n\ndef mi_binning(x, y, **kwargs):\n    x = digitize(x.T, **kwargs).T\n    y = digitize(y.T, **kwargs).T\n\n    return mi_binning_fcn(x, y)\n\n\n# list of estimators to compare\nmetrics = {\n    \"GC\": get_mi(\"gc\", biascorrect=False),\n    \"KNN-3\": get_mi(\"knn\", k=3),\n    \"KNN-10\": get_mi(\"knn\", k=10),\n    \"Kernel\": get_mi(\"kernel\"),\n    \"Binning\": partial(mi_binning, n_bins=4),\n}\n\n# number of samples to simulate data\nn_samples = np.geomspace(20, 1000, 10).astype(int)\n\n# number of repetitions to estimate the percentile interval\nn_repeat = 10\n\n\n# plotting function\ndef plot(mi, mi_theoric):\n    \"\"\"Plotting function.\"\"\"\n    for n_m, metric_name in enumerate(mi.keys()):\n        # get the entropies\n        x = mi[metric_name]\n\n        # get the color\n        color = f\"C{n_m}\"\n\n        # estimate lower and upper bounds of the [5, 95]th percentile interval\n        x_low, x_high = np.percentile(x, [5, 95], axis=0)\n\n        # plot the MI as a function of the number of samples and interval\n        plt.plot(n_samples, x.mean(0), color=color, lw=2, label=metric_name)\n        plt.fill_between(n_samples, x_low, x_high, color=color, alpha=0.2)\n\n    # plot the theoretical value\n    plt.axhline(mi_theoric, linestyle=\"--\", color=\"k\", label=\"Theoretical MI\")\n    plt.legend()\n    plt.xlabel(\"Number of samples\")\n    plt.ylabel(\"Mutual-information [bits]\")"
       ]
     },
     {

diff --git a/_downloads/f599913a46208f71dc47a6311a232f10/plot_syn_red_mmi.zip b/_downloads/f599913a46208f71dc47a6311a232f10/plot_syn_red_mmi.zip
diff --git a/_downloads/f97532cb8c6e95a143612bdc254c6548/plot_entropy_high_dimensional.zip b/_downloads/f97532cb8c6e95a143612bdc254c6548/plot_entropy_high_dimensional.zip
diff --git a/_modules/hoi/core/combinatory.html b/_modules/hoi/core/combinatory.html
@@ -256,6 +256,7 @@
 <li class="toctree-l3"><a class="reference internal" href="../../../api/generated/hoi.core.entropy_gc.html">hoi.core.entropy_gc</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/generated/hoi.core.entropy_gauss.html">hoi.core.entropy_gauss</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/generated/hoi.core.entropy_bin.html">hoi.core.entropy_bin</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../api/generated/hoi.core.entropy_hist.html">hoi.core.entropy_hist</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/generated/hoi.core.entropy_knn.html">hoi.core.entropy_knn</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/generated/hoi.core.entropy_kernel.html">hoi.core.entropy_kernel</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/generated/hoi.core.prepare_for_it.html">hoi.core.prepare_for_it</a></li>