[Add] PageRank 벡터의 모양을 반환하는 함수

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-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.7 (src)" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.8" project-jdk-type="Python SDK" />
 </project>
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@@ -2,7 +2,7 @@
 <module type="PYTHON_MODULE" version="4">
   <component name="NewModuleRootManager">
     <content url="file://$MODULE_DIR$" />
-    <orderEntry type="inheritedJdk" />
+    <orderEntry type="jdk" jdkName="Python 3.8" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
 </module>
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@@ -27,6 +27,13 @@
     <option name="HIGHLIGHT_NON_ACTIVE_CHANGELIST" value="false" />
     <option name="LAST_RESOLUTION" value="IGNORE" />
   </component>
+  <component name="FileTemplateManagerImpl">
+    <option name="RECENT_TEMPLATES">
+      <list>
+        <option value="Python Script" />
+      </list>
+    </option>
+  </component>
   <component name="Git.Settings">
     <option name="RECENT_GIT_ROOT_PATH" value="$PROJECT_DIR$/.." />
   </component>

+import numpy as np
+from sklearn.preprocessing import normalize
+
+def pagerank(x, df=0.85, max_iter=30, bias=None):
+    """
+    Arguments
+    ---------
+    x : scipy.sparse.csr_matrix
+        shape = (n vertex, n vertex)
+    df : float
+        Damping factor, 0 < df < 1
+    max_iter : int
+        Maximum number of iteration
+    bias : numpy.ndarray or None
+        If None, equal bias
+    Returns
+    -------
+    R : numpy.ndarray
+        PageRank vector. shape = (n vertex, 1)
+    """
+
+    assert 0 < df < 1
+
+    # initialize
+    A = normalize(x, axis=0, norm='l1')
+    R = np.ones(A.shape[0]).reshape(-1,1)
+
+    # check bias
+    if bias is None:
+        bias = (1 - df) * np.ones(A.shape[0]).reshape(-1,1)
+    else:
+        bias = bias.reshape(-1,1)
+        bias = A.shape[0] * bias / bias.sum()
+        assert bias.shape[0] == A.shape[0]
+        bias = (1 - df) * bias
+
+    # iteration
+    for _ in range(max_iter):
+        R = df * (A * R) + bias
+
+    return R
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