From 6be58390576c4a4089daaced2ca1d685df4bd7d7 Mon Sep 17 00:00:00 2001
From: nishikawa-screen <k.komatsu@screen.co.jp>
Date: Wed, 28 Jan 2026 14:01:44 +0900
Subject: [PATCH 1/4] =?UTF-8?q?Add=20an=20option=20to=20plot=20bootstrap?=
 =?UTF-8?q?=20coefficient=20histograms=20by=20causal=20structure=20(X?=
 =?UTF-8?q?=E2=86=92Y=20/=20Y=E2=86=92X=20/=20no=20edge).?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 examples/Bootstrap.ipynb | 195 ++++++++++++++++++++++++++++-----------
 1 file changed, 143 insertions(+), 52 deletions(-)

diff --git a/examples/Bootstrap.ipynb b/examples/Bootstrap.ipynb
index 9b9223f..cbd69f4 100644
--- a/examples/Bootstrap.ipynb
+++ b/examples/Bootstrap.ipynb
@@ -29,7 +29,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['1.24.4', '2.0.3', '0.20.1', '1.8.3']\n"
+      "['1.26.4', '2.3.3', '0.21', '1.12.1']\n"
      ]
     }
    ],
@@ -40,6 +40,9 @@
     "import lingam\n",
     "from lingam.utils import print_causal_directions, print_dagc, make_dot\n",
     "\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
     "print([np.__version__, pd.__version__, graphviz.__version__, lingam.__version__])\n",
     "\n",
     "np.set_printoptions(precision=3, suppress=True)\n",
@@ -184,104 +187,103 @@
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      "execution_count": 3,
@@ -319,8 +321,10 @@
    },
    "outputs": [],
    "source": [
+    "n_samples = 100\n",
+    "\n",
     "model = lingam.DirectLiNGAM()\n",
-    "result = model.bootstrap(X, n_sampling=100)"
+    "result = model.bootstrap(X, n_sampling=n_samples)"
    ]
   },
   {
@@ -378,7 +382,7 @@
     }
    ],
    "source": [
-    "print_causal_directions(cdc, 100)"
+    "print_causal_directions(cdc, n_samples)"
    ]
   },
   {
@@ -453,7 +457,7 @@
     }
    ],
    "source": [
-    "print_dagc(dagc, 100)"
+    "print_dagc(dagc, n_samples)"
    ]
   },
   {
@@ -1062,14 +1066,12 @@
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     {
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",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -1084,6 +1086,95 @@
     "plt.hist(result.total_effects_[:, to_index, from_index])"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Furthermore, when we separate the bootstrap coefficient distributions into the three structural cases — X→Y, Y→X, and no directed edge between X and Y — the resulting histograms are shown below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1500x400 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.ticker as ticker\n",
+    "\n",
+    "from_index, to_index = 0, 4\n",
+    "\n",
+    "te_xy = result.total_effects_[:, to_index, from_index]\n",
+    "te_yx = result.total_effects_[:, from_index, to_index]\n",
+    "\n",
+    "both_zero_mask = (te_xy == 0.0) & (te_yx == 0.0)\n",
+    "te_zero = result.total_effects_[both_zero_mask, to_index, from_index]\n",
+    "\n",
+    "te_xy = te_xy[te_xy != 0.0]\n",
+    "te_yx = te_yx[te_yx != 0.0]\n",
+    "\n",
+    "bins_count = int(np.ceil(1 + np.log2(max(n_samples, 1))))\n",
+    "\n",
+    "# calculate xmin, xmax\n",
+    "arr_list = [te_xy, te_yx, te_zero]\n",
+    "if any(a.size > 0 for a in arr_list):\n",
+    "    vals = np.concatenate([a for a in arr_list if a.size > 0])\n",
+    "else:\n",
+    "    vals = np.array([0.0])\n",
+    "\n",
+    "xmin, xmax = np.min(vals), np.max(vals)\n",
+    "if xmin == xmax:\n",
+    "    eps = 1e-9 if xmin == 0 else abs(xmin) * 1e-3\n",
+    "    xmin, xmax = xmin - eps, xmax + eps\n",
+    "\n",
+    "bin_edges = np.linspace(xmin, xmax, bins_count + 1)\n",
+    "\n",
+    "# calculate ymax\n",
+    "counts_xy, _ = np.histogram(te_xy, bins=bin_edges) if te_xy.size > 0 else (np.zeros(bins_count, dtype=int), None)\n",
+    "counts_yx, _ = np.histogram(te_yx, bins=bin_edges) if te_yx.size > 0 else (np.zeros(bins_count, dtype=int), None)\n",
+    "counts_zz, _ = np.histogram(te_zero, bins=bin_edges) if te_zero.size > 0 else (np.zeros(bins_count, dtype=int), None)\n",
+    "\n",
+    "ymax = int(max(counts_xy.max(initial=0), counts_yx.max(initial=0), counts_zz.max(initial=0)))\n",
+    "ymax = max(ymax, 1)\n",
+    "# If you want to set ymax to the number of bootstrap iterations, uncomment next line.\n",
+    "# ymax = n_samples\n",
+    "\n",
+    "# display histograms\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(15, 4), sharex=True, sharey=True)\n",
+    "labels = [f'x{i}' for i in range(X.shape[1])]\n",
+    "\n",
+    "axes[0].hist(te_xy, bins=bin_edges)\n",
+    "axes[0].set_title(f\"{labels[from_index]} -> {labels[to_index]}\")\n",
+    "axes[0].yaxis.set_major_locator(ticker.MaxNLocator(integer=True))\n",
+    "axes[0].set_xlim(xmin, xmax)\n",
+    "axes[0].set_ylim(0, ymax)\n",
+    "\n",
+    "axes[1].hist(te_yx, bins=bin_edges)\n",
+    "axes[1].set_title(f\"{labels[to_index]} -> {labels[from_index]}\")\n",
+    "axes[1].yaxis.set_major_locator(ticker.MaxNLocator(integer=True))\n",
+    "axes[1].set_xlim(xmin, xmax)\n",
+    "axes[1].set_ylim(0, ymax)\n",
+    "\n",
+    "axes[2].hist(te_zero, bins=bin_edges)\n",
+    "axes[2].set_title(\"No directed edge between \" + labels[from_index] + \" and \" + labels[to_index])\n",
+    "axes[2].yaxis.set_major_locator(ticker.MaxNLocator(integer=True))\n",
+    "axes[2].set_xlim(xmin, xmax)\n",
+    "axes[2].set_ylim(0, ymax)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1094,7 +1185,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2021-06-25T01:43:17.514898Z",
@@ -1200,14 +1291,14 @@
        "8  [3, 0, 5, 2, 1]  -2.719517         0.01"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "from_index = 3 # index of x3\n",
-    "to_index = 1 # index of x0\n",
+    "to_index = 1 # index of x1\n",
     "\n",
     "pd.DataFrame(result.get_paths(from_index, to_index))"
    ]
@@ -1215,7 +1306,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "lingam_env (3.11.9)",
    "language": "python",
    "name": "python3"
   },
@@ -1229,7 +1320,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.11.9"
   },
   "toc": {
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From 0d054053de4d4cb149e6d722bebe340c944cd564 Mon Sep 17 00:00:00 2001
From: nishikawa-screen <k.komatsu@screen.co.jp>
Date: Wed, 28 Jan 2026 14:01:56 +0900
Subject: [PATCH 2/4] Change coefficient estimation to a two-step median.

---
 examples/BootstrapWithImputation.ipynb | 223 +++++++++++++++++++------
 1 file changed, 173 insertions(+), 50 deletions(-)

diff --git a/examples/BootstrapWithImputation.ipynb b/examples/BootstrapWithImputation.ipynb
index 9d75069..0769c88 100644
--- a/examples/BootstrapWithImputation.ipynb
+++ b/examples/BootstrapWithImputation.ipynb
@@ -32,7 +32,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['1.24.4', '1.9.0']\n"
+      "['1.26.4', '1.12.1']\n"
      ]
     }
    ],
@@ -43,6 +43,9 @@
     "from lingam.utils import make_dot\n",
     "from lingam.tools import bootstrap_with_imputation\n",
     "\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "print([np.__version__, lingam.__version__])\n",
@@ -75,104 +78,103 @@
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    "outputs": [
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",
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",
       "text/plain": [
-       "<Figure size 1036.8x518.4 with 72 Axes>"
+       "<Figure size 1440x720 with 72 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -637,7 +637,7 @@
     "\n",
     "#### Median value of each element of the matrix\n",
     "\n",
-    "Median values of each element of the adjacency matrix estimated on the data with missing values are as follows:"
+    "The following matrix shows the median of each adjacency matrix element across all bootstrap replicates. For each bootstrap replicate, its representative adjacency matrix is obtained by taking the median across its multiple imputations."
    ]
   },
   {
@@ -655,8 +655,8 @@
        "       [ 0.555,  0.   ,  0.381,  0.   ,  0.   ,  0.   ],\n",
        "       [ 0.   ,  0.   ,  0.   ,  0.918,  0.   ,  0.   ],\n",
        "       [ 0.   ,  0.   ,  0.   ,  0.   ,  0.   ,  0.   ],\n",
-       "       [ 1.025,  0.   , -0.147,  0.   ,  0.   ,  0.   ],\n",
-       "       [ 0.892,  0.   ,  0.   ,  0.   ,  0.   ,  0.   ]])"
+       "       [ 1.026,  0.   , -0.148,  0.   ,  0.   ,  0.   ],\n",
+       "       [ 0.893,  0.   ,  0.   ,  0.   ,  0.   ,  0.   ]])"
       ]
      },
      "execution_count": 17,
@@ -665,7 +665,130 @@
     }
    ],
    "source": [
-    "np.median(adj_matrices_list, axis=(0, 1))"
+    "adj_median_per_bootstrap = np.median(adj_matrices_list, axis=1)\n",
+    "adj_median_over_bootstrap = np.median(adj_median_per_bootstrap, axis=0)\n",
+    "\n",
+    "adj_median_over_bootstrap"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "7a7e3abe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      ],
+      "text/plain": [
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+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "make_dot(adj_median_over_bootstrap)"
    ]
   },
   {
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  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "lingam_env (3.11.9)",
    "language": "python",
    "name": "python3"
   },
@@ -978,7 +1101,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.11.9"
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From a5e39b5fd30d8033b56bbab8a8f39e03b2b8e2d9 Mon Sep 17 00:00:00 2001
From: nishikawa-screen <k.komatsu@screen.co.jp>
Date: Wed, 28 Jan 2026 14:35:44 +0900
Subject: [PATCH 3/4] Update tutorial

---
 docs/image/bootstrap_hists.png              | Bin 0 -> 18037 bytes
 docs/tutorial/bootstrap.rst                 |  91 ++++++++++++++++++--
 docs/tutorial/bootstrap_with_imputation.rst |  19 ++--
 3 files changed, 97 insertions(+), 13 deletions(-)
 create mode 100644 docs/image/bootstrap_hists.png

diff --git a/docs/image/bootstrap_hists.png b/docs/image/bootstrap_hists.png
new file mode 100644
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diff --git a/docs/tutorial/bootstrap.rst b/docs/tutorial/bootstrap.rst
index 741cccf..15e6f44 100644
--- a/docs/tutorial/bootstrap.rst
+++ b/docs/tutorial/bootstrap.rst
@@ -15,6 +15,9 @@ In this example, we need to import ``numpy``, ``pandas``, and ``graphviz`` in ad
     import lingam
     from lingam.utils import print_causal_directions, print_dagc, make_dot
     
+    import warnings
+    warnings.filterwarnings("ignore")
+    
     print([np.__version__, pd.__version__, graphviz.__version__, lingam.__version__])
     
     np.set_printoptions(precision=3, suppress=True)
@@ -23,8 +26,8 @@ In this example, we need to import ``numpy``, ``pandas``, and ``graphviz`` in ad
 
 .. parsed-literal::
 
-    ['1.24.4', '2.0.3', '0.20.1', '1.8.3']
-
+    ['1.26.4', '2.3.3', '0.21', '1.12.1']
+    
 
 Test data
 ---------
@@ -174,8 +177,10 @@ We call :func:`~lingam.DirectLiNGAM.bootstrap` method instead of :func:`~lingam.
 
 .. code-block:: python
 
+    n_samples = 100
+    
     model = lingam.DirectLiNGAM()
-    result = model.bootstrap(X, n_sampling=100)
+    result = model.bootstrap(X, n_sampling=n_samples)
 
 Causal Directions
 -----------------
@@ -190,7 +195,7 @@ We can check the result by utility function.
 
 .. code-block:: python
 
-    print_causal_directions(cdc, 100)
+    print_causal_directions(cdc, n_samples)
 
 
 .. parsed-literal::
@@ -222,7 +227,7 @@ We can check the result by utility function.
 
 .. code-block:: python
 
-    print_dagc(dagc, 100)
+    print_dagc(dagc, n_samples)
 
 
 .. parsed-literal::
@@ -818,6 +823,80 @@ values of the causal effect, as shown below.
 
 .. image:: ../image/bootstrap_hist.png
 
+Furthermore, when we separate the bootstrap coefficient distributions
+into the three structural cases - X->Y, Y->X, and no directed edge between
+X and Y - the resulting histograms are shown below.
+
+.. code-block:: python
+
+    import matplotlib.ticker as ticker
+    
+    from_index, to_index = 0, 4
+    
+    te_xy = result.total_effects_[:, to_index, from_index]
+    te_yx = result.total_effects_[:, from_index, to_index]
+    
+    both_zero_mask = (te_xy == 0.0) & (te_yx == 0.0)
+    te_zero = result.total_effects_[both_zero_mask, to_index, from_index]
+    
+    te_xy = te_xy[te_xy != 0.0]
+    te_yx = te_yx[te_yx != 0.0]
+    
+    bins_count = int(np.ceil(1 + np.log2(max(n_samples, 1))))
+    
+    # calculate xmin, xmax
+    arr_list = [te_xy, te_yx, te_zero]
+    if any(a.size > 0 for a in arr_list):
+        vals = np.concatenate([a for a in arr_list if a.size > 0])
+    else:
+        vals = np.array([0.0])
+    
+    xmin, xmax = np.min(vals), np.max(vals)
+    if xmin == xmax:
+        eps = 1e-9 if xmin == 0 else abs(xmin) * 1e-3
+        xmin, xmax = xmin - eps, xmax + eps
+    
+    bin_edges = np.linspace(xmin, xmax, bins_count + 1)
+    
+    # calculate ymax
+    counts_xy, _ = np.histogram(te_xy, bins=bin_edges) if te_xy.size > 0 else (np.zeros(bins_count, dtype=int), None)
+    counts_yx, _ = np.histogram(te_yx, bins=bin_edges) if te_yx.size > 0 else (np.zeros(bins_count, dtype=int), None)
+    counts_zz, _ = np.histogram(te_zero, bins=bin_edges) if te_zero.size > 0 else (np.zeros(bins_count, dtype=int), None)
+    
+    ymax = int(max(counts_xy.max(initial=0), counts_yx.max(initial=0), counts_zz.max(initial=0)))
+    ymax = max(ymax, 1)
+    # If you want to set ymax to the number of bootstrap iterations, uncomment next line.
+    # ymax = n_samples
+    
+    # display histograms
+    fig, axes = plt.subplots(1, 3, figsize=(15, 4), sharex=True, sharey=True)
+    labels = [f'x{i}' for i in range(X.shape[1])]
+    
+    axes[0].hist(te_xy, bins=bin_edges)
+    axes[0].set_title(f"{labels[from_index]} -> {labels[to_index]}")
+    axes[0].yaxis.set_major_locator(ticker.MaxNLocator(integer=True))
+    axes[0].set_xlim(xmin, xmax)
+    axes[0].set_ylim(0, ymax)
+    
+    axes[1].hist(te_yx, bins=bin_edges)
+    axes[1].set_title(f"{labels[to_index]} -> {labels[from_index]}")
+    axes[1].yaxis.set_major_locator(ticker.MaxNLocator(integer=True))
+    axes[1].set_xlim(xmin, xmax)
+    axes[1].set_ylim(0, ymax)
+    
+    axes[2].hist(te_zero, bins=bin_edges)
+    axes[2].set_title("No directed edge between " + labels[from_index] + " and " + labels[to_index])
+    axes[2].yaxis.set_major_locator(ticker.MaxNLocator(integer=True))
+    axes[2].set_xlim(xmin, xmax)
+    axes[2].set_ylim(0, ymax)
+    
+    plt.tight_layout()
+    plt.show()
+
+
+
+.. image:: ../image/bootstrap_hists.png
+
 Bootstrap Probability of Path
 -----------------------------
 
@@ -830,7 +909,7 @@ variable X0 to variable X1.
 .. code-block:: python
 
     from_index = 3 # index of x3
-    to_index = 1 # index of x0
+    to_index = 1 # index of x1
     
     pd.DataFrame(result.get_paths(from_index, to_index))
 
diff --git a/docs/tutorial/bootstrap_with_imputation.rst b/docs/tutorial/bootstrap_with_imputation.rst
index 4df14f0..3a596fe 100644
--- a/docs/tutorial/bootstrap_with_imputation.rst
+++ b/docs/tutorial/bootstrap_with_imputation.rst
@@ -28,8 +28,8 @@ Import and settings
 
 .. parsed-literal::
 
-    ['1.24.4', '1.8.3']
-
+    ['1.26.4', '1.12.1']
+    
 
 Test data
 ---------
@@ -311,12 +311,17 @@ results with no missing data.
 Median value of each element of the matrix
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-Median values of each element of the adjacency matrix estimated on the
-data with missing values are as follows:
+The following matrix shows the median of each adjacency matrix element
+across all bootstrap replicates. For each bootstrap replicate, its
+representative adjacency matrix is obtained by taking the median across
+its multiple imputations.
 
 .. code-block:: python
 
-    np.median(adj_matrices_list, axis=(0, 1))
+    adj_median_per_bootstrap = np.median(adj_matrices_list, axis=1)
+    adj_median_over_bootstrap = np.median(adj_median_per_bootstrap, axis=0)
+    
+    adj_median_over_bootstrap
 
 
 
@@ -327,8 +332,8 @@ data with missing values are as follows:
            [ 0.555,  0.   ,  0.381,  0.   ,  0.   ,  0.   ],
            [ 0.   ,  0.   ,  0.   ,  0.918,  0.   ,  0.   ],
            [ 0.   ,  0.   ,  0.   ,  0.   ,  0.   ,  0.   ],
-           [ 1.025,  0.   , -0.147,  0.   ,  0.   ,  0.   ],
-           [ 0.892,  0.   ,  0.   ,  0.   ,  0.   ,  0.   ]])
+           [ 1.026,  0.   , -0.148,  0.   ,  0.   ,  0.   ],
+           [ 0.893,  0.   ,  0.   ,  0.   ,  0.   ,  0.   ]])
 
 
 

From 0b33f49016be023d38982eb0ef559bf03d018044 Mon Sep 17 00:00:00 2001
From: nishikawa-screen <k.komatsu@screen.co.jp>
Date: Wed, 28 Jan 2026 16:50:26 +0900
Subject: [PATCH 4/4] Update tutorial and example

---
 docs/image/bootstrap_hist.png  | Bin 5971 -> 8965 bytes
 docs/image/bootstrap_hists.png | Bin 18037 -> 19104 bytes
 docs/tutorial/bootstrap.rst    | 645 ++++++++++++++++++---------
 examples/Bootstrap.ipynb       | 792 ++++++++++++++++++++++-----------
 4 files changed, 980 insertions(+), 457 deletions(-)

diff --git a/docs/image/bootstrap_hist.png b/docs/image/bootstrap_hist.png
index 921268850b3beb655832464504266603992330dd..c81c9617ce19d51b8ff7f6274417dd3cde9d2ce5 100644
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CrYwp8

diff --git a/docs/tutorial/bootstrap.rst b/docs/tutorial/bootstrap.rst
index 15e6f44..cf8c3a4 100644
--- a/docs/tutorial/bootstrap.rst
+++ b/docs/tutorial/bootstrap.rst
@@ -36,12 +36,13 @@ We create test data consisting of 6 variables.
 
 .. code-block:: python
 
-    x3 = np.random.uniform(size=1000)
-    x0 = 3.0*x3 + np.random.uniform(size=1000)
-    x2 = 6.0*x3 + np.random.uniform(size=1000)
-    x1 = 3.0*x0 + 2.0*x2 + np.random.uniform(size=1000)
-    x5 = 4.0*x0 + np.random.uniform(size=1000)
-    x4 = 8.0*x0 - 1.0*x2 + np.random.uniform(size=1000)
+    _size = 100
+    x3 = np.random.uniform(size=_size)
+    x0 = 3.0*x3 + np.random.uniform(size=_size)
+    x2 = 6.0*x3 + np.random.uniform(size=_size)
+    x1 = 3.0*x0 + 2.0*x2 + np.random.uniform(size=_size)
+    x5 = 4.0*x0 + np.random.uniform(size=_size)
+    x4 = 8.0*x0 - 1.0*x2 + np.random.uniform(size=_size)
     X = pd.DataFrame(np.array([x0, x1, x2, x3, x4, x5]).T ,columns=['x0', 'x1', 'x2', 'x3', 'x4', 'x5'])
     X.head()
 
@@ -102,48 +103,48 @@ We create test data consisting of 6 variables.
       <tbody>
         <tr>
           <th>0</th>
-          <td>2.239321</td>
-          <td>15.340724</td>
-          <td>4.104399</td>
+          <td>2.324257</td>
+          <td>15.088680</td>
+          <td>3.604677</td>
           <td>0.548814</td>
-          <td>14.176947</td>
-          <td>9.249925</td>
+          <td>15.299760</td>
+          <td>9.698288</td>
         </tr>
         <tr>
           <th>1</th>
-          <td>2.155632</td>
-          <td>16.630954</td>
-          <td>4.767220</td>
+          <td>2.415576</td>
+          <td>17.995735</td>
+          <td>4.987480</td>
           <td>0.715189</td>
-          <td>12.775458</td>
-          <td>9.189045</td>
+          <td>14.710164</td>
+          <td>10.591596</td>
         </tr>
         <tr>
           <th>2</th>
-          <td>2.284116</td>
-          <td>15.910406</td>
-          <td>4.139736</td>
+          <td>2.543484</td>
+          <td>15.952262</td>
+          <td>3.994332</td>
           <td>0.602763</td>
-          <td>14.201794</td>
-          <td>9.273880</td>
+          <td>16.878512</td>
+          <td>10.273552</td>
         </tr>
         <tr>
           <th>3</th>
-          <td>2.343420</td>
-          <td>14.921457</td>
-          <td>3.519820</td>
+          <td>2.596838</td>
+          <td>14.769421</td>
+          <td>3.448903</td>
           <td>0.544883</td>
-          <td>15.580067</td>
-          <td>9.723392</td>
+          <td>18.076397</td>
+          <td>11.332654</td>
         </tr>
         <tr>
           <th>4</th>
-          <td>1.314940</td>
-          <td>11.055176</td>
-          <td>3.146972</td>
+          <td>1.519718</td>
+          <td>10.099609</td>
+          <td>2.566608</td>
           <td>0.423655</td>
-          <td>7.604743</td>
-          <td>5.312976</td>
+          <td>9.924640</td>
+          <td>6.948359</td>
         </tr>
       </tbody>
     </table>
@@ -177,7 +178,7 @@ We call :func:`~lingam.DirectLiNGAM.bootstrap` method instead of :func:`~lingam.
 
 .. code-block:: python
 
-    n_samples = 100
+    n_samples = 1000
     
     model = lingam.DirectLiNGAM()
     result = model.bootstrap(X, n_sampling=n_samples)
@@ -200,15 +201,15 @@ We can check the result by utility function.
 
 .. parsed-literal::
 
-    x5 <--- x0 (b>0) (100.0%)
-    x1 <--- x0 (b>0) (100.0%)
-    x1 <--- x2 (b>0) (100.0%)
-    x4 <--- x2 (b<0) (100.0%)
-    x0 <--- x3 (b>0) (98.0%)
-    x4 <--- x0 (b>0) (98.0%)
-    x2 <--- x3 (b>0) (96.0%)
-    x3 <--- x2 (b>0) (4.0%)
-
+    x4 <--- x2 (b<0) (87.9%)
+    x4 <--- x0 (b>0) (86.6%)
+    x1 <--- x2 (b>0) (77.5%)
+    x1 <--- x0 (b>0) (77.3%)
+    x2 <--- x3 (b>0) (76.1%)
+    x5 <--- x0 (b>0) (75.4%)
+    x0 <--- x3 (b>0) (45.4%)
+    x0 <--- x5 (b>0) (24.6%)
+    
 
 Directed Acyclic Graphs
 -----------------------
@@ -232,7 +233,7 @@ We can check the result by utility function.
 
 .. parsed-literal::
 
-    DAG[0]: 84.0%
+    DAG[0]: 17.0%
     	x0 <--- x3 (b>0)
     	x1 <--- x0 (b>0)
     	x1 <--- x2 (b>0)
@@ -240,7 +241,7 @@ We can check the result by utility function.
     	x4 <--- x0 (b>0)
     	x4 <--- x2 (b<0)
     	x5 <--- x0 (b>0)
-    DAG[1]: 3.0%
+    DAG[1]: 4.2%
     	x0 <--- x3 (b>0)
     	x1 <--- x0 (b>0)
     	x1 <--- x2 (b>0)
@@ -248,16 +249,15 @@ We can check the result by utility function.
     	x4 <--- x0 (b>0)
     	x4 <--- x2 (b<0)
     	x5 <--- x0 (b>0)
-    DAG[2]: 2.0%
-    	x0 <--- x3 (b>0)
+    DAG[2]: 3.9%
     	x1 <--- x0 (b>0)
     	x1 <--- x2 (b>0)
-    	x1 <--- x3 (b<0)
     	x2 <--- x3 (b>0)
+    	x3 <--- x0 (b>0)
     	x4 <--- x0 (b>0)
     	x4 <--- x2 (b<0)
     	x5 <--- x0 (b>0)
-
+    
 
 Probability
 -----------
@@ -273,13 +273,13 @@ bootstrapping.
 
 .. parsed-literal::
 
-    [[0.   0.   0.03 0.98 0.02 0.  ]
-     [1.   0.   1.   0.02 0.   0.01]
-     [0.01 0.   0.   0.96 0.   0.01]
-     [0.   0.   0.04 0.   0.   0.  ]
-     [0.98 0.01 1.   0.02 0.   0.02]
-     [1.   0.   0.02 0.02 0.   0.  ]]
-
+    [[0.    0.178 0.163 0.482 0.134 0.246]
+     [0.773 0.    0.775 0.202 0.069 0.064]
+     [0.2   0.225 0.    0.761 0.093 0.032]
+     [0.183 0.166 0.19  0.    0.031 0.084]
+     [0.866 0.074 0.88  0.121 0.    0.043]
+     [0.754 0.059 0.065 0.095 0.062 0.   ]]
+    
 
 Total Causal Effects
 --------------------
@@ -356,150 +356,213 @@ below.
       <tbody>
         <tr>
           <th>0</th>
-          <td>x3</td>
-          <td>x0</td>
-          <td>3.004106</td>
-          <td>1.00</td>
+          <td>x2</td>
+          <td>x4</td>
+          <td>-0.986006</td>
+          <td>0.884</td>
         </tr>
         <tr>
           <th>1</th>
           <td>x0</td>
-          <td>x1</td>
-          <td>2.963177</td>
-          <td>1.00</td>
+          <td>x4</td>
+          <td>7.975821</td>
+          <td>0.866</td>
         </tr>
         <tr>
           <th>2</th>
-          <td>x2</td>
-          <td>x1</td>
-          <td>2.017539</td>
-          <td>1.00</td>
+          <td>x3</td>
+          <td>x4</td>
+          <td>17.169757</td>
+          <td>0.858</td>
         </tr>
         <tr>
           <th>3</th>
           <td>x3</td>
           <td>x1</td>
-          <td>20.928254</td>
-          <td>1.00</td>
+          <td>20.553538</td>
+          <td>0.794</td>
         </tr>
         <tr>
           <th>4</th>
           <td>x0</td>
-          <td>x5</td>
-          <td>3.997787</td>
-          <td>1.00</td>
+          <td>x1</td>
+          <td>3.020369</td>
+          <td>0.793</td>
         </tr>
         <tr>
           <th>5</th>
           <td>x3</td>
-          <td>x4</td>
-          <td>18.077943</td>
-          <td>1.00</td>
+          <td>x2</td>
+          <td>5.968590</td>
+          <td>0.788</td>
         </tr>
         <tr>
           <th>6</th>
-          <td>x3</td>
-          <td>x5</td>
-          <td>12.012988</td>
-          <td>1.00</td>
+          <td>x2</td>
+          <td>x1</td>
+          <td>1.992771</td>
+          <td>0.775</td>
         </tr>
         <tr>
           <th>7</th>
-          <td>x2</td>
-          <td>x4</td>
-          <td>-1.006362</td>
-          <td>1.00</td>
+          <td>x0</td>
+          <td>x5</td>
+          <td>3.984278</td>
+          <td>0.754</td>
         </tr>
         <tr>
           <th>8</th>
-          <td>x0</td>
-          <td>x4</td>
-          <td>8.011818</td>
-          <td>0.98</td>
+          <td>x3</td>
+          <td>x5</td>
+          <td>11.686617</td>
+          <td>0.657</td>
         </tr>
         <tr>
           <th>9</th>
           <td>x3</td>
-          <td>x2</td>
-          <td>5.964879</td>
-          <td>0.96</td>
+          <td>x0</td>
+          <td>2.920996</td>
+          <td>0.653</td>
         </tr>
         <tr>
           <th>10</th>
+          <td>x0</td>
           <td>x2</td>
-          <td>x5</td>
-          <td>0.396327</td>
-          <td>0.09</td>
+          <td>1.679845</td>
+          <td>0.343</td>
         </tr>
         <tr>
           <th>11</th>
           <td>x2</td>
-          <td>x0</td>
-          <td>0.487915</td>
-          <td>0.07</td>
+          <td>x5</td>
+          <td>0.155444</td>
+          <td>0.282</td>
         </tr>
         <tr>
           <th>12</th>
-          <td>x2</td>
-          <td>x3</td>
-          <td>0.164565</td>
-          <td>0.04</td>
+          <td>x5</td>
+          <td>x4</td>
+          <td>1.550997</td>
+          <td>0.266</td>
         </tr>
         <tr>
           <th>13</th>
-          <td>x5</td>
-          <td>x4</td>
-          <td>0.087437</td>
-          <td>0.03</td>
+          <td>x0</td>
+          <td>x3</td>
+          <td>0.305366</td>
+          <td>0.260</td>
         </tr>
         <tr>
           <th>14</th>
-          <td>x4</td>
           <td>x5</td>
-          <td>0.496445</td>
-          <td>0.02</td>
+          <td>x1</td>
+          <td>0.939446</td>
+          <td>0.259</td>
         </tr>
         <tr>
           <th>15</th>
           <td>x5</td>
-          <td>x1</td>
-          <td>-0.064703</td>
-          <td>0.02</td>
+          <td>x0</td>
+          <td>0.249365</td>
+          <td>0.246</td>
         </tr>
         <tr>
           <th>16</th>
-          <td>x4</td>
           <td>x1</td>
-          <td>0.367100</td>
-          <td>0.02</td>
+          <td>x4</td>
+          <td>0.863039</td>
+          <td>0.245</td>
         </tr>
         <tr>
           <th>17</th>
-          <td>x4</td>
+          <td>x2</td>
           <td>x0</td>
-          <td>0.124114</td>
-          <td>0.02</td>
+          <td>0.120842</td>
+          <td>0.244</td>
         </tr>
         <tr>
           <th>18</th>
-          <td>x0</td>
+          <td>x1</td>
           <td>x2</td>
-          <td>0.056261</td>
-          <td>0.01</td>
+          <td>0.285349</td>
+          <td>0.225</td>
         </tr>
         <tr>
           <th>19</th>
           <td>x1</td>
-          <td>x4</td>
-          <td>-0.097108</td>
-          <td>0.01</td>
+          <td>x5</td>
+          <td>0.576121</td>
+          <td>0.199</td>
         </tr>
         <tr>
           <th>20</th>
+          <td>x1</td>
+          <td>x0</td>
+          <td>0.144407</td>
+          <td>0.197</td>
+        </tr>
+        <tr>
+          <th>21</th>
           <td>x5</td>
           <td>x2</td>
-          <td>-0.111894</td>
-          <td>0.01</td>
+          <td>0.451434</td>
+          <td>0.196</td>
+        </tr>
+        <tr>
+          <th>22</th>
+          <td>x1</td>
+          <td>x3</td>
+          <td>0.046961</td>
+          <td>0.194</td>
+        </tr>
+        <tr>
+          <th>23</th>
+          <td>x2</td>
+          <td>x3</td>
+          <td>0.133917</td>
+          <td>0.191</td>
+        </tr>
+        <tr>
+          <th>24</th>
+          <td>x5</td>
+          <td>x3</td>
+          <td>0.076654</td>
+          <td>0.168</td>
+        </tr>
+        <tr>
+          <th>25</th>
+          <td>x4</td>
+          <td>x1</td>
+          <td>0.362045</td>
+          <td>0.144</td>
+        </tr>
+        <tr>
+          <th>26</th>
+          <td>x4</td>
+          <td>x5</td>
+          <td>0.478376</td>
+          <td>0.143</td>
+        </tr>
+        <tr>
+          <th>27</th>
+          <td>x4</td>
+          <td>x0</td>
+          <td>0.123534</td>
+          <td>0.134</td>
+        </tr>
+        <tr>
+          <th>28</th>
+          <td>x4</td>
+          <td>x2</td>
+          <td>-0.139721</td>
+          <td>0.097</td>
+        </tr>
+        <tr>
+          <th>29</th>
+          <td>x4</td>
+          <td>x3</td>
+          <td>-0.006454</td>
+          <td>0.043</td>
         </tr>
       </tbody>
     </table>
@@ -570,36 +633,36 @@ We can easily perform sorting operations with pandas.DataFrame.
           <th>3</th>
           <td>x3</td>
           <td>x1</td>
-          <td>20.928254</td>
-          <td>1.00</td>
+          <td>20.553538</td>
+          <td>0.794</td>
         </tr>
         <tr>
-          <th>5</th>
+          <th>2</th>
           <td>x3</td>
           <td>x4</td>
-          <td>18.077943</td>
-          <td>1.00</td>
+          <td>17.169757</td>
+          <td>0.858</td>
         </tr>
         <tr>
-          <th>6</th>
+          <th>8</th>
           <td>x3</td>
           <td>x5</td>
-          <td>12.012988</td>
-          <td>1.00</td>
+          <td>11.686617</td>
+          <td>0.657</td>
         </tr>
         <tr>
-          <th>8</th>
+          <th>1</th>
           <td>x0</td>
           <td>x4</td>
-          <td>8.011818</td>
-          <td>0.98</td>
+          <td>7.975821</td>
+          <td>0.866</td>
         </tr>
         <tr>
-          <th>9</th>
+          <th>5</th>
           <td>x3</td>
           <td>x2</td>
-          <td>5.964879</td>
-          <td>0.96</td>
+          <td>5.968590</td>
+          <td>0.788</td>
         </tr>
       </tbody>
     </table>
@@ -665,39 +728,39 @@ We can easily perform sorting operations with pandas.DataFrame.
       </thead>
       <tbody>
         <tr>
-          <th>20</th>
-          <td>x5</td>
-          <td>x2</td>
-          <td>-0.111894</td>
-          <td>0.01</td>
+          <th>29</th>
+          <td>x4</td>
+          <td>x3</td>
+          <td>-0.006454</td>
+          <td>0.043</td>
         </tr>
         <tr>
-          <th>18</th>
-          <td>x0</td>
+          <th>28</th>
+          <td>x4</td>
           <td>x2</td>
-          <td>0.056261</td>
-          <td>0.01</td>
+          <td>-0.139721</td>
+          <td>0.097</td>
         </tr>
         <tr>
-          <th>19</th>
-          <td>x1</td>
+          <th>27</th>
           <td>x4</td>
-          <td>-0.097108</td>
-          <td>0.01</td>
+          <td>x0</td>
+          <td>0.123534</td>
+          <td>0.134</td>
         </tr>
         <tr>
-          <th>17</th>
+          <th>26</th>
           <td>x4</td>
-          <td>x0</td>
-          <td>0.124114</td>
-          <td>0.02</td>
+          <td>x5</td>
+          <td>0.478376</td>
+          <td>0.143</td>
         </tr>
         <tr>
-          <th>16</th>
+          <th>25</th>
           <td>x4</td>
           <td>x1</td>
-          <td>0.367100</td>
-          <td>0.02</td>
+          <td>0.362045</td>
+          <td>0.144</td>
         </tr>
       </tbody>
     </table>
@@ -766,39 +829,39 @@ following code extracts the causal direction towards x1.
       </thead>
       <tbody>
         <tr>
-          <th>1</th>
-          <td>x0</td>
+          <th>3</th>
+          <td>x3</td>
           <td>x1</td>
-          <td>2.963177</td>
-          <td>1.00</td>
+          <td>20.553538</td>
+          <td>0.794</td>
         </tr>
         <tr>
-          <th>2</th>
-          <td>x2</td>
+          <th>4</th>
+          <td>x0</td>
           <td>x1</td>
-          <td>2.017539</td>
-          <td>1.00</td>
+          <td>3.020369</td>
+          <td>0.793</td>
         </tr>
         <tr>
-          <th>3</th>
-          <td>x3</td>
+          <th>6</th>
+          <td>x2</td>
           <td>x1</td>
-          <td>20.928254</td>
-          <td>1.00</td>
+          <td>1.992771</td>
+          <td>0.775</td>
         </tr>
         <tr>
-          <th>15</th>
+          <th>14</th>
           <td>x5</td>
           <td>x1</td>
-          <td>-0.064703</td>
-          <td>0.02</td>
+          <td>0.939446</td>
+          <td>0.259</td>
         </tr>
         <tr>
-          <th>16</th>
+          <th>25</th>
           <td>x4</td>
           <td>x1</td>
-          <td>0.367100</td>
-          <td>0.02</td>
+          <td>0.362045</td>
+          <td>0.144</td>
         </tr>
       </tbody>
     </table>
@@ -831,7 +894,7 @@ X and Y - the resulting histograms are shown below.
 
     import matplotlib.ticker as ticker
     
-    from_index, to_index = 0, 4
+    from_index, to_index = 2, 4
     
     te_xy = result.total_effects_[:, to_index, from_index]
     te_yx = result.total_effects_[:, from_index, to_index]
@@ -893,8 +956,6 @@ X and Y - the resulting histograms are shown below.
     plt.tight_layout()
     plt.show()
 
-
-
 .. image:: ../image/bootstrap_hists.png
 
 Bootstrap Probability of Path
@@ -967,57 +1028,237 @@ variable X0 to variable X1.
       <tbody>
         <tr>
           <th>0</th>
-          <td>[3, 0, 1]</td>
-          <td>8.893562</td>
-          <td>0.98</td>
+          <td>[3, 2, 1]</td>
+          <td>11.914854</td>
+          <td>0.660</td>
         </tr>
         <tr>
           <th>1</th>
-          <td>[3, 2, 1]</td>
-          <td>12.030408</td>
-          <td>0.96</td>
+          <td>[3, 0, 1]</td>
+          <td>8.756234</td>
+          <td>0.443</td>
         </tr>
         <tr>
           <th>2</th>
-          <td>[3, 2, 0, 1]</td>
-          <td>2.239175</td>
-          <td>0.03</td>
+          <td>[3, 1]</td>
+          <td>2.105700</td>
+          <td>0.202</td>
         </tr>
         <tr>
           <th>3</th>
-          <td>[3, 1]</td>
-          <td>-0.639462</td>
-          <td>0.02</td>
+          <td>[3, 2, 0, 1]</td>
+          <td>1.635862</td>
+          <td>0.094</td>
         </tr>
         <tr>
           <th>4</th>
-          <td>[3, 2, 4, 0, 1]</td>
-          <td>-3.194541</td>
-          <td>0.02</td>
+          <td>[3, 5, 0, 1]</td>
+          <td>8.670284</td>
+          <td>0.060</td>
         </tr>
         <tr>
           <th>5</th>
           <td>[3, 4, 0, 1]</td>
-          <td>9.820705</td>
-          <td>0.02</td>
+          <td>6.979752</td>
+          <td>0.054</td>
         </tr>
         <tr>
           <th>6</th>
-          <td>[3, 0, 2, 1]</td>
-          <td>3.061033</td>
-          <td>0.01</td>
+          <td>[3, 2, 4, 1]</td>
+          <td>-1.146483</td>
+          <td>0.038</td>
         </tr>
         <tr>
           <th>7</th>
-          <td>[3, 0, 5, 1]</td>
-          <td>1.176834</td>
-          <td>0.01</td>
+          <td>[3, 0, 4, 1]</td>
+          <td>4.459602</td>
+          <td>0.028</td>
         </tr>
         <tr>
           <th>8</th>
+          <td>[3, 0, 5, 1]</td>
+          <td>2.864025</td>
+          <td>0.026</td>
+        </tr>
+        <tr>
+          <th>9</th>
+          <td>[3, 2, 4, 0, 1]</td>
+          <td>-4.602396</td>
+          <td>0.024</td>
+        </tr>
+        <tr>
+          <th>10</th>
+          <td>[3, 0, 2, 1]</td>
+          <td>-1.512156</td>
+          <td>0.022</td>
+        </tr>
+        <tr>
+          <th>11</th>
+          <td>[3, 4, 1]</td>
+          <td>4.954881</td>
+          <td>0.019</td>
+        </tr>
+        <tr>
+          <th>12</th>
+          <td>[3, 2, 5, 0, 1]</td>
+          <td>0.374461</td>
+          <td>0.009</td>
+        </tr>
+        <tr>
+          <th>13</th>
+          <td>[3, 2, 0, 5, 1]</td>
+          <td>0.583856</td>
+          <td>0.008</td>
+        </tr>
+        <tr>
+          <th>14</th>
+          <td>[3, 5, 4, 0, 1]</td>
+          <td>6.941594</td>
+          <td>0.007</td>
+        </tr>
+        <tr>
+          <th>15</th>
+          <td>[3, 4, 5, 0, 1]</td>
+          <td>2.145360</td>
+          <td>0.007</td>
+        </tr>
+        <tr>
+          <th>16</th>
+          <td>[3, 4, 2, 1]</td>
+          <td>-1.080988</td>
+          <td>0.007</td>
+        </tr>
+        <tr>
+          <th>17</th>
+          <td>[3, 5, 1]</td>
+          <td>3.272935</td>
+          <td>0.006</td>
+        </tr>
+        <tr>
+          <th>18</th>
+          <td>[3, 4, 0, 5, 1]</td>
+          <td>2.697207</td>
+          <td>0.005</td>
+        </tr>
+        <tr>
+          <th>19</th>
+          <td>[3, 4, 2, 0, 1]</td>
+          <td>-0.219167</td>
+          <td>0.005</td>
+        </tr>
+        <tr>
+          <th>20</th>
           <td>[3, 0, 5, 2, 1]</td>
-          <td>-2.719517</td>
-          <td>0.01</td>
+          <td>5.181321</td>
+          <td>0.004</td>
+        </tr>
+        <tr>
+          <th>21</th>
+          <td>[3, 5, 0, 4, 1]</td>
+          <td>5.442240</td>
+          <td>0.004</td>
+        </tr>
+        <tr>
+          <th>22</th>
+          <td>[3, 5, 2, 1]</td>
+          <td>1.537410</td>
+          <td>0.003</td>
+        </tr>
+        <tr>
+          <th>23</th>
+          <td>[3, 4, 5, 1]</td>
+          <td>4.166390</td>
+          <td>0.003</td>
+        </tr>
+        <tr>
+          <th>24</th>
+          <td>[3, 0, 5, 4, 1]</td>
+          <td>-0.522766</td>
+          <td>0.003</td>
+        </tr>
+        <tr>
+          <th>25</th>
+          <td>[3, 2, 4, 5, 0, 1]</td>
+          <td>-1.083415</td>
+          <td>0.003</td>
+        </tr>
+        <tr>
+          <th>26</th>
+          <td>[3, 5, 4, 1]</td>
+          <td>-7.351469</td>
+          <td>0.002</td>
+        </tr>
+        <tr>
+          <th>27</th>
+          <td>[3, 2, 4, 5, 1]</td>
+          <td>0.203801</td>
+          <td>0.002</td>
+        </tr>
+        <tr>
+          <th>28</th>
+          <td>[3, 2, 4, 0, 5, 1]</td>
+          <td>-1.303056</td>
+          <td>0.002</td>
+        </tr>
+        <tr>
+          <th>29</th>
+          <td>[3, 5, 2, 0, 1]</td>
+          <td>-0.006054</td>
+          <td>0.002</td>
+        </tr>
+        <tr>
+          <th>30</th>
+          <td>[3, 5, 4, 2, 1]</td>
+          <td>-15.137090</td>
+          <td>0.002</td>
+        </tr>
+        <tr>
+          <th>31</th>
+          <td>[3, 4, 0, 2, 1]</td>
+          <td>-3.885974</td>
+          <td>0.001</td>
+        </tr>
+        <tr>
+          <th>32</th>
+          <td>[3, 2, 5, 4, 1]</td>
+          <td>-0.035426</td>
+          <td>0.001</td>
+        </tr>
+        <tr>
+          <th>33</th>
+          <td>[3, 5, 0, 2, 1]</td>
+          <td>7.112032</td>
+          <td>0.001</td>
+        </tr>
+        <tr>
+          <th>34</th>
+          <td>[3, 2, 0, 4, 1]</td>
+          <td>-3.206907</td>
+          <td>0.001</td>
+        </tr>
+        <tr>
+          <th>35</th>
+          <td>[3, 5, 2, 4, 0, 1]</td>
+          <td>0.351331</td>
+          <td>0.001</td>
+        </tr>
+        <tr>
+          <th>36</th>
+          <td>[3, 0, 4, 5, 1]</td>
+          <td>-0.695107</td>
+          <td>0.001</td>
+        </tr>
+        <tr>
+          <th>37</th>
+          <td>[3, 5, 4, 0, 2, 1]</td>
+          <td>14.386599</td>
+          <td>0.001</td>
+        </tr>
+        <tr>
+          <th>38</th>
+          <td>[3, 4, 2, 0, 5, 1]</td>
+          <td>-0.072976</td>
+          <td>0.001</td>
         </tr>
       </tbody>
     </table>
diff --git a/examples/Bootstrap.ipynb b/examples/Bootstrap.ipynb
index cbd69f4..e5be0ab 100644
--- a/examples/Bootstrap.ipynb
+++ b/examples/Bootstrap.ipynb
@@ -99,60 +99,60 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>2.239321</td>\n",
-       "      <td>15.340724</td>\n",
-       "      <td>4.104399</td>\n",
+       "      <td>2.324257</td>\n",
+       "      <td>15.088680</td>\n",
+       "      <td>3.604677</td>\n",
        "      <td>0.548814</td>\n",
-       "      <td>14.176947</td>\n",
-       "      <td>9.249925</td>\n",
+       "      <td>15.299760</td>\n",
+       "      <td>9.698288</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>2.155632</td>\n",
-       "      <td>16.630954</td>\n",
-       "      <td>4.767220</td>\n",
+       "      <td>2.415576</td>\n",
+       "      <td>17.995735</td>\n",
+       "      <td>4.987480</td>\n",
        "      <td>0.715189</td>\n",
-       "      <td>12.775458</td>\n",
-       "      <td>9.189045</td>\n",
+       "      <td>14.710164</td>\n",
+       "      <td>10.591596</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>2.284116</td>\n",
-       "      <td>15.910406</td>\n",
-       "      <td>4.139736</td>\n",
+       "      <td>2.543484</td>\n",
+       "      <td>15.952262</td>\n",
+       "      <td>3.994332</td>\n",
        "      <td>0.602763</td>\n",
-       "      <td>14.201794</td>\n",
-       "      <td>9.273880</td>\n",
+       "      <td>16.878512</td>\n",
+       "      <td>10.273552</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>2.343420</td>\n",
-       "      <td>14.921457</td>\n",
-       "      <td>3.519820</td>\n",
+       "      <td>2.596838</td>\n",
+       "      <td>14.769421</td>\n",
+       "      <td>3.448903</td>\n",
        "      <td>0.544883</td>\n",
-       "      <td>15.580067</td>\n",
-       "      <td>9.723392</td>\n",
+       "      <td>18.076397</td>\n",
+       "      <td>11.332654</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>1.314940</td>\n",
-       "      <td>11.055176</td>\n",
-       "      <td>3.146972</td>\n",
+       "      <td>1.519718</td>\n",
+       "      <td>10.099609</td>\n",
+       "      <td>2.566608</td>\n",
        "      <td>0.423655</td>\n",
-       "      <td>7.604743</td>\n",
-       "      <td>5.312976</td>\n",
+       "      <td>9.924640</td>\n",
+       "      <td>6.948359</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "         x0         x1        x2        x3         x4        x5\n",
-       "0  2.239321  15.340724  4.104399  0.548814  14.176947  9.249925\n",
-       "1  2.155632  16.630954  4.767220  0.715189  12.775458  9.189045\n",
-       "2  2.284116  15.910406  4.139736  0.602763  14.201794  9.273880\n",
-       "3  2.343420  14.921457  3.519820  0.544883  15.580067  9.723392\n",
-       "4  1.314940  11.055176  3.146972  0.423655   7.604743  5.312976"
+       "         x0         x1        x2        x3         x4         x5\n",
+       "0  2.324257  15.088680  3.604677  0.548814  15.299760   9.698288\n",
+       "1  2.415576  17.995735  4.987480  0.715189  14.710164  10.591596\n",
+       "2  2.543484  15.952262  3.994332  0.602763  16.878512  10.273552\n",
+       "3  2.596838  14.769421  3.448903  0.544883  18.076397  11.332654\n",
+       "4  1.519718  10.099609  2.566608  0.423655   9.924640   6.948359"
       ]
      },
      "execution_count": 2,
@@ -161,12 +161,13 @@
     }
    ],
    "source": [
-    "x3 = np.random.uniform(size=1000)\n",
-    "x0 = 3.0*x3 + np.random.uniform(size=1000)\n",
-    "x2 = 6.0*x3 + np.random.uniform(size=1000)\n",
-    "x1 = 3.0*x0 + 2.0*x2 + np.random.uniform(size=1000)\n",
-    "x5 = 4.0*x0 + np.random.uniform(size=1000)\n",
-    "x4 = 8.0*x0 - 1.0*x2 + np.random.uniform(size=1000)\n",
+    "_size = 100\n",
+    "x3 = np.random.uniform(size=_size)\n",
+    "x0 = 3.0*x3 + np.random.uniform(size=_size)\n",
+    "x2 = 6.0*x3 + np.random.uniform(size=_size)\n",
+    "x1 = 3.0*x0 + 2.0*x2 + np.random.uniform(size=_size)\n",
+    "x5 = 4.0*x0 + np.random.uniform(size=_size)\n",
+    "x4 = 8.0*x0 - 1.0*x2 + np.random.uniform(size=_size)\n",
     "X = pd.DataFrame(np.array([x0, x1, x2, x3, x4, x5]).T ,columns=['x0', 'x1', 'x2', 'x3', 'x4', 'x5'])\n",
     "X.head()"
    ]
@@ -283,7 +284,7 @@
        "</svg>\n"
       ],
       "text/plain": [
-       "<graphviz.graphs.Digraph at 0x1da70ea3e50>"
+       "<graphviz.graphs.Digraph at 0x208ee5d3a50>"
       ]
      },
      "execution_count": 3,
@@ -321,7 +322,7 @@
    },
    "outputs": [],
    "source": [
-    "n_samples = 100\n",
+    "n_samples = 1000\n",
     "\n",
     "model = lingam.DirectLiNGAM()\n",
     "result = model.bootstrap(X, n_sampling=n_samples)"
@@ -370,14 +371,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "x5 <--- x0 (b>0) (100.0%)\n",
-      "x1 <--- x0 (b>0) (100.0%)\n",
-      "x1 <--- x2 (b>0) (100.0%)\n",
-      "x4 <--- x2 (b<0) (100.0%)\n",
-      "x0 <--- x3 (b>0) (98.0%)\n",
-      "x4 <--- x0 (b>0) (98.0%)\n",
-      "x2 <--- x3 (b>0) (96.0%)\n",
-      "x3 <--- x2 (b>0) (4.0%)\n"
+      "x4 <--- x2 (b<0) (87.9%)\n",
+      "x4 <--- x0 (b>0) (86.6%)\n",
+      "x1 <--- x2 (b>0) (77.5%)\n",
+      "x1 <--- x0 (b>0) (77.3%)\n",
+      "x2 <--- x3 (b>0) (76.1%)\n",
+      "x5 <--- x0 (b>0) (75.4%)\n",
+      "x0 <--- x3 (b>0) (45.4%)\n",
+      "x0 <--- x5 (b>0) (24.6%)\n"
      ]
     }
    ],
@@ -428,7 +429,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "DAG[0]: 84.0%\n",
+      "DAG[0]: 17.0%\n",
       "\tx0 <--- x3 (b>0)\n",
       "\tx1 <--- x0 (b>0)\n",
       "\tx1 <--- x2 (b>0)\n",
@@ -436,7 +437,7 @@
       "\tx4 <--- x0 (b>0)\n",
       "\tx4 <--- x2 (b<0)\n",
       "\tx5 <--- x0 (b>0)\n",
-      "DAG[1]: 3.0%\n",
+      "DAG[1]: 4.2%\n",
       "\tx0 <--- x3 (b>0)\n",
       "\tx1 <--- x0 (b>0)\n",
       "\tx1 <--- x2 (b>0)\n",
@@ -444,12 +445,11 @@
       "\tx4 <--- x0 (b>0)\n",
       "\tx4 <--- x2 (b<0)\n",
       "\tx5 <--- x0 (b>0)\n",
-      "DAG[2]: 2.0%\n",
-      "\tx0 <--- x3 (b>0)\n",
+      "DAG[2]: 3.9%\n",
       "\tx1 <--- x0 (b>0)\n",
       "\tx1 <--- x2 (b>0)\n",
-      "\tx1 <--- x3 (b<0)\n",
       "\tx2 <--- x3 (b>0)\n",
+      "\tx3 <--- x0 (b>0)\n",
       "\tx4 <--- x0 (b>0)\n",
       "\tx4 <--- x2 (b<0)\n",
       "\tx5 <--- x0 (b>0)\n"
@@ -482,12 +482,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[0.   0.   0.03 0.98 0.02 0.  ]\n",
-      " [1.   0.   1.   0.02 0.   0.01]\n",
-      " [0.01 0.   0.   0.96 0.   0.01]\n",
-      " [0.   0.   0.04 0.   0.   0.  ]\n",
-      " [0.98 0.01 1.   0.02 0.   0.02]\n",
-      " [1.   0.   0.02 0.02 0.   0.  ]]\n"
+      "[[0.    0.178 0.163 0.482 0.134 0.246]\n",
+      " [0.773 0.    0.775 0.202 0.069 0.064]\n",
+      " [0.2   0.225 0.    0.761 0.093 0.032]\n",
+      " [0.183 0.166 0.19  0.    0.031 0.084]\n",
+      " [0.866 0.074 0.88  0.121 0.    0.043]\n",
+      " [0.754 0.059 0.065 0.095 0.062 0.   ]]\n"
      ]
     }
    ],
@@ -545,150 +545,213 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>x3</td>\n",
-       "      <td>x0</td>\n",
-       "      <td>3.004106</td>\n",
-       "      <td>1.00</td>\n",
+       "      <td>x2</td>\n",
+       "      <td>x4</td>\n",
+       "      <td>-0.986006</td>\n",
+       "      <td>0.884</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>x0</td>\n",
-       "      <td>x1</td>\n",
-       "      <td>2.963177</td>\n",
-       "      <td>1.00</td>\n",
+       "      <td>x4</td>\n",
+       "      <td>7.975821</td>\n",
+       "      <td>0.866</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>x2</td>\n",
-       "      <td>x1</td>\n",
-       "      <td>2.017539</td>\n",
-       "      <td>1.00</td>\n",
+       "      <td>x3</td>\n",
+       "      <td>x4</td>\n",
+       "      <td>17.169757</td>\n",
+       "      <td>0.858</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>x3</td>\n",
        "      <td>x1</td>\n",
-       "      <td>20.928254</td>\n",
-       "      <td>1.00</td>\n",
+       "      <td>20.553538</td>\n",
+       "      <td>0.794</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>x0</td>\n",
-       "      <td>x5</td>\n",
-       "      <td>3.997787</td>\n",
-       "      <td>1.00</td>\n",
+       "      <td>x1</td>\n",
+       "      <td>3.020369</td>\n",
+       "      <td>0.793</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>x3</td>\n",
-       "      <td>x4</td>\n",
-       "      <td>18.077943</td>\n",
-       "      <td>1.00</td>\n",
+       "      <td>x2</td>\n",
+       "      <td>5.968590</td>\n",
+       "      <td>0.788</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>x3</td>\n",
-       "      <td>x5</td>\n",
-       "      <td>12.012988</td>\n",
-       "      <td>1.00</td>\n",
+       "      <td>x2</td>\n",
+       "      <td>x1</td>\n",
+       "      <td>1.992771</td>\n",
+       "      <td>0.775</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
-       "      <td>x2</td>\n",
-       "      <td>x4</td>\n",
-       "      <td>-1.006362</td>\n",
-       "      <td>1.00</td>\n",
+       "      <td>x0</td>\n",
+       "      <td>x5</td>\n",
+       "      <td>3.984278</td>\n",
+       "      <td>0.754</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
-       "      <td>x0</td>\n",
-       "      <td>x4</td>\n",
-       "      <td>8.011818</td>\n",
-       "      <td>0.98</td>\n",
+       "      <td>x3</td>\n",
+       "      <td>x5</td>\n",
+       "      <td>11.686617</td>\n",
+       "      <td>0.657</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>x3</td>\n",
-       "      <td>x2</td>\n",
-       "      <td>5.964879</td>\n",
-       "      <td>0.96</td>\n",
+       "      <td>x0</td>\n",
+       "      <td>2.920996</td>\n",
+       "      <td>0.653</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
+       "      <td>x0</td>\n",
        "      <td>x2</td>\n",
-       "      <td>x5</td>\n",
-       "      <td>0.396327</td>\n",
-       "      <td>0.09</td>\n",
+       "      <td>1.679845</td>\n",
+       "      <td>0.343</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td>x2</td>\n",
-       "      <td>x0</td>\n",
-       "      <td>0.487915</td>\n",
-       "      <td>0.07</td>\n",
+       "      <td>x5</td>\n",
+       "      <td>0.155444</td>\n",
+       "      <td>0.282</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
-       "      <td>x2</td>\n",
-       "      <td>x3</td>\n",
-       "      <td>0.164565</td>\n",
-       "      <td>0.04</td>\n",
+       "      <td>x5</td>\n",
+       "      <td>x4</td>\n",
+       "      <td>1.550997</td>\n",
+       "      <td>0.266</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
-       "      <td>x5</td>\n",
-       "      <td>x4</td>\n",
-       "      <td>0.087437</td>\n",
-       "      <td>0.03</td>\n",
+       "      <td>x0</td>\n",
+       "      <td>x3</td>\n",
+       "      <td>0.305366</td>\n",
+       "      <td>0.260</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
-       "      <td>x4</td>\n",
        "      <td>x5</td>\n",
-       "      <td>0.496445</td>\n",
-       "      <td>0.02</td>\n",
+       "      <td>x1</td>\n",
+       "      <td>0.939446</td>\n",
+       "      <td>0.259</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td>x5</td>\n",
-       "      <td>x1</td>\n",
-       "      <td>-0.064703</td>\n",
-       "      <td>0.02</td>\n",
+       "      <td>x0</td>\n",
+       "      <td>0.249365</td>\n",
+       "      <td>0.246</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
-       "      <td>x4</td>\n",
        "      <td>x1</td>\n",
-       "      <td>0.367100</td>\n",
-       "      <td>0.02</td>\n",
+       "      <td>x4</td>\n",
+       "      <td>0.863039</td>\n",
+       "      <td>0.245</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
-       "      <td>x4</td>\n",
+       "      <td>x2</td>\n",
        "      <td>x0</td>\n",
-       "      <td>0.124114</td>\n",
-       "      <td>0.02</td>\n",
+       "      <td>0.120842</td>\n",
+       "      <td>0.244</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
-       "      <td>x0</td>\n",
+       "      <td>x1</td>\n",
        "      <td>x2</td>\n",
-       "      <td>0.056261</td>\n",
-       "      <td>0.01</td>\n",
+       "      <td>0.285349</td>\n",
+       "      <td>0.225</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td>x1</td>\n",
-       "      <td>x4</td>\n",
-       "      <td>-0.097108</td>\n",
-       "      <td>0.01</td>\n",
+       "      <td>x5</td>\n",
+       "      <td>0.576121</td>\n",
+       "      <td>0.199</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
+       "      <td>x1</td>\n",
+       "      <td>x0</td>\n",
+       "      <td>0.144407</td>\n",
+       "      <td>0.197</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
        "      <td>x5</td>\n",
        "      <td>x2</td>\n",
-       "      <td>-0.111894</td>\n",
-       "      <td>0.01</td>\n",
+       "      <td>0.451434</td>\n",
+       "      <td>0.196</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>x1</td>\n",
+       "      <td>x3</td>\n",
+       "      <td>0.046961</td>\n",
+       "      <td>0.194</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>x2</td>\n",
+       "      <td>x3</td>\n",
+       "      <td>0.133917</td>\n",
+       "      <td>0.191</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>x5</td>\n",
+       "      <td>x3</td>\n",
+       "      <td>0.076654</td>\n",
+       "      <td>0.168</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>x4</td>\n",
+       "      <td>x1</td>\n",
+       "      <td>0.362045</td>\n",
+       "      <td>0.144</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>x4</td>\n",
+       "      <td>x5</td>\n",
+       "      <td>0.478376</td>\n",
+       "      <td>0.143</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>x4</td>\n",
+       "      <td>x0</td>\n",
+       "      <td>0.123534</td>\n",
+       "      <td>0.134</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>x4</td>\n",
+       "      <td>x2</td>\n",
+       "      <td>-0.139721</td>\n",
+       "      <td>0.097</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>x4</td>\n",
+       "      <td>x3</td>\n",
+       "      <td>-0.006454</td>\n",
+       "      <td>0.043</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -696,27 +759,36 @@
       ],
       "text/plain": [
        "   from  to     effect  probability\n",
-       "0    x3  x0   3.004106         1.00\n",
-       "1    x0  x1   2.963177         1.00\n",
-       "2    x2  x1   2.017539         1.00\n",
-       "3    x3  x1  20.928254         1.00\n",
-       "4    x0  x5   3.997787         1.00\n",
-       "5    x3  x4  18.077943         1.00\n",
-       "6    x3  x5  12.012988         1.00\n",
-       "7    x2  x4  -1.006362         1.00\n",
-       "8    x0  x4   8.011818         0.98\n",
-       "9    x3  x2   5.964879         0.96\n",
-       "10   x2  x5   0.396327         0.09\n",
-       "11   x2  x0   0.487915         0.07\n",
-       "12   x2  x3   0.164565         0.04\n",
-       "13   x5  x4   0.087437         0.03\n",
-       "14   x4  x5   0.496445         0.02\n",
-       "15   x5  x1  -0.064703         0.02\n",
-       "16   x4  x1   0.367100         0.02\n",
-       "17   x4  x0   0.124114         0.02\n",
-       "18   x0  x2   0.056261         0.01\n",
-       "19   x1  x4  -0.097108         0.01\n",
-       "20   x5  x2  -0.111894         0.01"
+       "0    x2  x4  -0.986006        0.884\n",
+       "1    x0  x4   7.975821        0.866\n",
+       "2    x3  x4  17.169757        0.858\n",
+       "3    x3  x1  20.553538        0.794\n",
+       "4    x0  x1   3.020369        0.793\n",
+       "5    x3  x2   5.968590        0.788\n",
+       "6    x2  x1   1.992771        0.775\n",
+       "7    x0  x5   3.984278        0.754\n",
+       "8    x3  x5  11.686617        0.657\n",
+       "9    x3  x0   2.920996        0.653\n",
+       "10   x0  x2   1.679845        0.343\n",
+       "11   x2  x5   0.155444        0.282\n",
+       "12   x5  x4   1.550997        0.266\n",
+       "13   x0  x3   0.305366        0.260\n",
+       "14   x5  x1   0.939446        0.259\n",
+       "15   x5  x0   0.249365        0.246\n",
+       "16   x1  x4   0.863039        0.245\n",
+       "17   x2  x0   0.120842        0.244\n",
+       "18   x1  x2   0.285349        0.225\n",
+       "19   x1  x5   0.576121        0.199\n",
+       "20   x1  x0   0.144407        0.197\n",
+       "21   x5  x2   0.451434        0.196\n",
+       "22   x1  x3   0.046961        0.194\n",
+       "23   x2  x3   0.133917        0.191\n",
+       "24   x5  x3   0.076654        0.168\n",
+       "25   x4  x1   0.362045        0.144\n",
+       "26   x4  x5   0.478376        0.143\n",
+       "27   x4  x0   0.123534        0.134\n",
+       "28   x4  x2  -0.139721        0.097\n",
+       "29   x4  x3  -0.006454        0.043"
       ]
      },
      "execution_count": 10,
@@ -784,36 +856,36 @@
        "      <th>3</th>\n",
        "      <td>x3</td>\n",
        "      <td>x1</td>\n",
-       "      <td>20.928254</td>\n",
-       "      <td>1.00</td>\n",
+       "      <td>20.553538</td>\n",
+       "      <td>0.794</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5</th>\n",
+       "      <th>2</th>\n",
        "      <td>x3</td>\n",
        "      <td>x4</td>\n",
-       "      <td>18.077943</td>\n",
-       "      <td>1.00</td>\n",
+       "      <td>17.169757</td>\n",
+       "      <td>0.858</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6</th>\n",
+       "      <th>8</th>\n",
        "      <td>x3</td>\n",
        "      <td>x5</td>\n",
-       "      <td>12.012988</td>\n",
-       "      <td>1.00</td>\n",
+       "      <td>11.686617</td>\n",
+       "      <td>0.657</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>8</th>\n",
+       "      <th>1</th>\n",
        "      <td>x0</td>\n",
        "      <td>x4</td>\n",
-       "      <td>8.011818</td>\n",
-       "      <td>0.98</td>\n",
+       "      <td>7.975821</td>\n",
+       "      <td>0.866</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9</th>\n",
+       "      <th>5</th>\n",
        "      <td>x3</td>\n",
        "      <td>x2</td>\n",
-       "      <td>5.964879</td>\n",
-       "      <td>0.96</td>\n",
+       "      <td>5.968590</td>\n",
+       "      <td>0.788</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -821,11 +893,11 @@
       ],
       "text/plain": [
        "  from  to     effect  probability\n",
-       "3   x3  x1  20.928254         1.00\n",
-       "5   x3  x4  18.077943         1.00\n",
-       "6   x3  x5  12.012988         1.00\n",
-       "8   x0  x4   8.011818         0.98\n",
-       "9   x3  x2   5.964879         0.96"
+       "3   x3  x1  20.553538        0.794\n",
+       "2   x3  x4  17.169757        0.858\n",
+       "8   x3  x5  11.686617        0.657\n",
+       "1   x0  x4   7.975821        0.866\n",
+       "5   x3  x2   5.968590        0.788"
       ]
      },
      "execution_count": 11,
@@ -876,39 +948,39 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>x5</td>\n",
-       "      <td>x2</td>\n",
-       "      <td>-0.111894</td>\n",
-       "      <td>0.01</td>\n",
+       "      <th>29</th>\n",
+       "      <td>x4</td>\n",
+       "      <td>x3</td>\n",
+       "      <td>-0.006454</td>\n",
+       "      <td>0.043</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>x0</td>\n",
+       "      <th>28</th>\n",
+       "      <td>x4</td>\n",
        "      <td>x2</td>\n",
-       "      <td>0.056261</td>\n",
-       "      <td>0.01</td>\n",
+       "      <td>-0.139721</td>\n",
+       "      <td>0.097</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>x1</td>\n",
+       "      <th>27</th>\n",
        "      <td>x4</td>\n",
-       "      <td>-0.097108</td>\n",
-       "      <td>0.01</td>\n",
+       "      <td>x0</td>\n",
+       "      <td>0.123534</td>\n",
+       "      <td>0.134</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>17</th>\n",
+       "      <th>26</th>\n",
        "      <td>x4</td>\n",
-       "      <td>x0</td>\n",
-       "      <td>0.124114</td>\n",
-       "      <td>0.02</td>\n",
+       "      <td>x5</td>\n",
+       "      <td>0.478376</td>\n",
+       "      <td>0.143</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>16</th>\n",
+       "      <th>25</th>\n",
        "      <td>x4</td>\n",
        "      <td>x1</td>\n",
-       "      <td>0.367100</td>\n",
-       "      <td>0.02</td>\n",
+       "      <td>0.362045</td>\n",
+       "      <td>0.144</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -916,11 +988,11 @@
       ],
       "text/plain": [
        "   from  to    effect  probability\n",
-       "20   x5  x2 -0.111894         0.01\n",
-       "18   x0  x2  0.056261         0.01\n",
-       "19   x1  x4 -0.097108         0.01\n",
-       "17   x4  x0  0.124114         0.02\n",
-       "16   x4  x1  0.367100         0.02"
+       "29   x4  x3 -0.006454        0.043\n",
+       "28   x4  x2 -0.139721        0.097\n",
+       "27   x4  x0  0.123534        0.134\n",
+       "26   x4  x5  0.478376        0.143\n",
+       "25   x4  x1  0.362045        0.144"
       ]
      },
      "execution_count": 12,
@@ -978,39 +1050,39 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>x0</td>\n",
+       "      <th>3</th>\n",
+       "      <td>x3</td>\n",
        "      <td>x1</td>\n",
-       "      <td>2.963177</td>\n",
-       "      <td>1.00</td>\n",
+       "      <td>20.553538</td>\n",
+       "      <td>0.794</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>x2</td>\n",
+       "      <th>4</th>\n",
+       "      <td>x0</td>\n",
        "      <td>x1</td>\n",
-       "      <td>2.017539</td>\n",
-       "      <td>1.00</td>\n",
+       "      <td>3.020369</td>\n",
+       "      <td>0.793</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>x3</td>\n",
+       "      <th>6</th>\n",
+       "      <td>x2</td>\n",
        "      <td>x1</td>\n",
-       "      <td>20.928254</td>\n",
-       "      <td>1.00</td>\n",
+       "      <td>1.992771</td>\n",
+       "      <td>0.775</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>15</th>\n",
+       "      <th>14</th>\n",
        "      <td>x5</td>\n",
        "      <td>x1</td>\n",
-       "      <td>-0.064703</td>\n",
-       "      <td>0.02</td>\n",
+       "      <td>0.939446</td>\n",
+       "      <td>0.259</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>16</th>\n",
+       "      <th>25</th>\n",
        "      <td>x4</td>\n",
        "      <td>x1</td>\n",
-       "      <td>0.367100</td>\n",
-       "      <td>0.02</td>\n",
+       "      <td>0.362045</td>\n",
+       "      <td>0.144</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1018,11 +1090,11 @@
       ],
       "text/plain": [
        "   from  to     effect  probability\n",
-       "1    x0  x1   2.963177         1.00\n",
-       "2    x2  x1   2.017539         1.00\n",
-       "3    x3  x1  20.928254         1.00\n",
-       "15   x5  x1  -0.064703         0.02\n",
-       "16   x4  x1   0.367100         0.02"
+       "3    x3  x1  20.553538        0.794\n",
+       "4    x0  x1   3.020369        0.793\n",
+       "6    x2  x1   1.992771        0.775\n",
+       "14   x5  x1   0.939446        0.259\n",
+       "25   x4  x1   0.362045        0.144"
       ]
      },
      "execution_count": 13,
@@ -1054,9 +1126,9 @@
     {
      "data": {
       "text/plain": [
-       "(array([ 1.,  8., 10., 17., 19., 20., 13.,  9.,  1.,  2.]),\n",
-       " array([2.936, 2.951, 2.965, 2.98 , 2.994, 3.008, 3.023, 3.037, 3.051,\n",
-       "        3.066, 3.08 ]),\n",
+       "(array([ 26.,  18., 348.,  13.,   0.,   4., 570.,   7.,  12.,   2.]),\n",
+       " array([-2.185, -1.413, -0.641,  0.131,  0.904,  1.676,  2.448,  3.22 ,\n",
+       "         3.993,  4.765,  5.537]),\n",
        " <BarContainer object of 10 artists>)"
       ]
      },
@@ -1066,7 +1138,7 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1100,7 +1172,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1500x400 with 3 Axes>"
       ]
@@ -1112,7 +1184,7 @@
    "source": [
     "import matplotlib.ticker as ticker\n",
     "\n",
-    "from_index, to_index = 0, 4\n",
+    "from_index, to_index = 2, 4\n",
     "\n",
     "te_xy = result.total_effects_[:, to_index, from_index]\n",
     "te_yx = result.total_effects_[:, from_index, to_index]\n",
@@ -1222,73 +1294,283 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>[3, 0, 1]</td>\n",
-       "      <td>8.893562</td>\n",
-       "      <td>0.98</td>\n",
+       "      <td>[3, 2, 1]</td>\n",
+       "      <td>11.914854</td>\n",
+       "      <td>0.660</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>[3, 2, 1]</td>\n",
-       "      <td>12.030408</td>\n",
-       "      <td>0.96</td>\n",
+       "      <td>[3, 0, 1]</td>\n",
+       "      <td>8.756234</td>\n",
+       "      <td>0.443</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>[3, 2, 0, 1]</td>\n",
-       "      <td>2.239175</td>\n",
-       "      <td>0.03</td>\n",
+       "      <td>[3, 1]</td>\n",
+       "      <td>2.105700</td>\n",
+       "      <td>0.202</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>[3, 1]</td>\n",
-       "      <td>-0.639462</td>\n",
-       "      <td>0.02</td>\n",
+       "      <td>[3, 2, 0, 1]</td>\n",
+       "      <td>1.635862</td>\n",
+       "      <td>0.094</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>[3, 2, 4, 0, 1]</td>\n",
-       "      <td>-3.194541</td>\n",
-       "      <td>0.02</td>\n",
+       "      <td>[3, 5, 0, 1]</td>\n",
+       "      <td>8.670284</td>\n",
+       "      <td>0.060</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>[3, 4, 0, 1]</td>\n",
-       "      <td>9.820705</td>\n",
-       "      <td>0.02</td>\n",
+       "      <td>6.979752</td>\n",
+       "      <td>0.054</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>[3, 0, 2, 1]</td>\n",
-       "      <td>3.061033</td>\n",
-       "      <td>0.01</td>\n",
+       "      <td>[3, 2, 4, 1]</td>\n",
+       "      <td>-1.146483</td>\n",
+       "      <td>0.038</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
-       "      <td>[3, 0, 5, 1]</td>\n",
-       "      <td>1.176834</td>\n",
-       "      <td>0.01</td>\n",
+       "      <td>[3, 0, 4, 1]</td>\n",
+       "      <td>4.459602</td>\n",
+       "      <td>0.028</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
+       "      <td>[3, 0, 5, 1]</td>\n",
+       "      <td>2.864025</td>\n",
+       "      <td>0.026</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>[3, 2, 4, 0, 1]</td>\n",
+       "      <td>-4.602396</td>\n",
+       "      <td>0.024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>[3, 0, 2, 1]</td>\n",
+       "      <td>-1.512156</td>\n",
+       "      <td>0.022</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>[3, 4, 1]</td>\n",
+       "      <td>4.954881</td>\n",
+       "      <td>0.019</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>[3, 2, 5, 0, 1]</td>\n",
+       "      <td>0.374461</td>\n",
+       "      <td>0.009</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>[3, 2, 0, 5, 1]</td>\n",
+       "      <td>0.583856</td>\n",
+       "      <td>0.008</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>[3, 5, 4, 0, 1]</td>\n",
+       "      <td>6.941594</td>\n",
+       "      <td>0.007</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>[3, 4, 5, 0, 1]</td>\n",
+       "      <td>2.145360</td>\n",
+       "      <td>0.007</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>[3, 4, 2, 1]</td>\n",
+       "      <td>-1.080988</td>\n",
+       "      <td>0.007</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>[3, 5, 1]</td>\n",
+       "      <td>3.272935</td>\n",
+       "      <td>0.006</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>[3, 4, 0, 5, 1]</td>\n",
+       "      <td>2.697207</td>\n",
+       "      <td>0.005</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>[3, 4, 2, 0, 1]</td>\n",
+       "      <td>-0.219167</td>\n",
+       "      <td>0.005</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
        "      <td>[3, 0, 5, 2, 1]</td>\n",
-       "      <td>-2.719517</td>\n",
-       "      <td>0.01</td>\n",
+       "      <td>5.181321</td>\n",
+       "      <td>0.004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>[3, 5, 0, 4, 1]</td>\n",
+       "      <td>5.442240</td>\n",
+       "      <td>0.004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>[3, 5, 2, 1]</td>\n",
+       "      <td>1.537410</td>\n",
+       "      <td>0.003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>[3, 4, 5, 1]</td>\n",
+       "      <td>4.166390</td>\n",
+       "      <td>0.003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>[3, 0, 5, 4, 1]</td>\n",
+       "      <td>-0.522766</td>\n",
+       "      <td>0.003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>[3, 2, 4, 5, 0, 1]</td>\n",
+       "      <td>-1.083415</td>\n",
+       "      <td>0.003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>[3, 5, 4, 1]</td>\n",
+       "      <td>-7.351469</td>\n",
+       "      <td>0.002</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>[3, 2, 4, 5, 1]</td>\n",
+       "      <td>0.203801</td>\n",
+       "      <td>0.002</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>[3, 2, 4, 0, 5, 1]</td>\n",
+       "      <td>-1.303056</td>\n",
+       "      <td>0.002</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>[3, 5, 2, 0, 1]</td>\n",
+       "      <td>-0.006054</td>\n",
+       "      <td>0.002</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>[3, 5, 4, 2, 1]</td>\n",
+       "      <td>-15.137090</td>\n",
+       "      <td>0.002</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>[3, 4, 0, 2, 1]</td>\n",
+       "      <td>-3.885974</td>\n",
+       "      <td>0.001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>[3, 2, 5, 4, 1]</td>\n",
+       "      <td>-0.035426</td>\n",
+       "      <td>0.001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>[3, 5, 0, 2, 1]</td>\n",
+       "      <td>7.112032</td>\n",
+       "      <td>0.001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>[3, 2, 0, 4, 1]</td>\n",
+       "      <td>-3.206907</td>\n",
+       "      <td>0.001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>[3, 5, 2, 4, 0, 1]</td>\n",
+       "      <td>0.351331</td>\n",
+       "      <td>0.001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>[3, 0, 4, 5, 1]</td>\n",
+       "      <td>-0.695107</td>\n",
+       "      <td>0.001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>[3, 5, 4, 0, 2, 1]</td>\n",
+       "      <td>14.386599</td>\n",
+       "      <td>0.001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>[3, 4, 2, 0, 5, 1]</td>\n",
+       "      <td>-0.072976</td>\n",
+       "      <td>0.001</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "              path     effect  probability\n",
-       "0        [3, 0, 1]   8.893562         0.98\n",
-       "1        [3, 2, 1]  12.030408         0.96\n",
-       "2     [3, 2, 0, 1]   2.239175         0.03\n",
-       "3           [3, 1]  -0.639462         0.02\n",
-       "4  [3, 2, 4, 0, 1]  -3.194541         0.02\n",
-       "5     [3, 4, 0, 1]   9.820705         0.02\n",
-       "6     [3, 0, 2, 1]   3.061033         0.01\n",
-       "7     [3, 0, 5, 1]   1.176834         0.01\n",
-       "8  [3, 0, 5, 2, 1]  -2.719517         0.01"
+       "                  path     effect  probability\n",
+       "0            [3, 2, 1]  11.914854        0.660\n",
+       "1            [3, 0, 1]   8.756234        0.443\n",
+       "2               [3, 1]   2.105700        0.202\n",
+       "3         [3, 2, 0, 1]   1.635862        0.094\n",
+       "4         [3, 5, 0, 1]   8.670284        0.060\n",
+       "5         [3, 4, 0, 1]   6.979752        0.054\n",
+       "6         [3, 2, 4, 1]  -1.146483        0.038\n",
+       "7         [3, 0, 4, 1]   4.459602        0.028\n",
+       "8         [3, 0, 5, 1]   2.864025        0.026\n",
+       "9      [3, 2, 4, 0, 1]  -4.602396        0.024\n",
+       "10        [3, 0, 2, 1]  -1.512156        0.022\n",
+       "11           [3, 4, 1]   4.954881        0.019\n",
+       "12     [3, 2, 5, 0, 1]   0.374461        0.009\n",
+       "13     [3, 2, 0, 5, 1]   0.583856        0.008\n",
+       "14     [3, 5, 4, 0, 1]   6.941594        0.007\n",
+       "15     [3, 4, 5, 0, 1]   2.145360        0.007\n",
+       "16        [3, 4, 2, 1]  -1.080988        0.007\n",
+       "17           [3, 5, 1]   3.272935        0.006\n",
+       "18     [3, 4, 0, 5, 1]   2.697207        0.005\n",
+       "19     [3, 4, 2, 0, 1]  -0.219167        0.005\n",
+       "20     [3, 0, 5, 2, 1]   5.181321        0.004\n",
+       "21     [3, 5, 0, 4, 1]   5.442240        0.004\n",
+       "22        [3, 5, 2, 1]   1.537410        0.003\n",
+       "23        [3, 4, 5, 1]   4.166390        0.003\n",
+       "24     [3, 0, 5, 4, 1]  -0.522766        0.003\n",
+       "25  [3, 2, 4, 5, 0, 1]  -1.083415        0.003\n",
+       "26        [3, 5, 4, 1]  -7.351469        0.002\n",
+       "27     [3, 2, 4, 5, 1]   0.203801        0.002\n",
+       "28  [3, 2, 4, 0, 5, 1]  -1.303056        0.002\n",
+       "29     [3, 5, 2, 0, 1]  -0.006054        0.002\n",
+       "30     [3, 5, 4, 2, 1] -15.137090        0.002\n",
+       "31     [3, 4, 0, 2, 1]  -3.885974        0.001\n",
+       "32     [3, 2, 5, 4, 1]  -0.035426        0.001\n",
+       "33     [3, 5, 0, 2, 1]   7.112032        0.001\n",
+       "34     [3, 2, 0, 4, 1]  -3.206907        0.001\n",
+       "35  [3, 5, 2, 4, 0, 1]   0.351331        0.001\n",
+       "36     [3, 0, 4, 5, 1]  -0.695107        0.001\n",
+       "37  [3, 5, 4, 0, 2, 1]  14.386599        0.001\n",
+       "38  [3, 4, 2, 0, 5, 1]  -0.072976        0.001"
       ]
      },
      "execution_count": 16,