Initial commit

Author: mohanr

_posts/2023-19-09-Data-Streaming-Algorithms.md

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+---
+layout: post
+title: Data Streaming Algorithms
+published: true
+---
+
+I hope to add details as I understand this field better. 
+I  may also use multiple languages to code parts of algorithms.
+
+# Sketching
+
+
+# Hadamard Transform
+
+
+
+{% highlight python %}
+import tensorflow as tf
+
+def hademard(M):
+    i = tf.constant(1)
+    loop_var = int(tf.math.log(tf.cast(tf.shape(M)[0],tf.float32))/tf.math.log(2.0))
+    c = lambda i, d: tf.less_equal(i, tf.cond(loop_var < 1 , lambda: 1, lambda : loop_var))
+    T = tf.constant([[1,1],[1,-1]],dtype=tf.float32)
+    
+    def logic(i, H1):
+        operator_1 = tf.linalg.LinearOperatorFullMatrix(H1)
+        operator_2 = tf.linalg.LinearOperatorFullMatrix(T/2)
+        operator = tf.linalg.LinearOperatorKronecker([operator_1, operator_2])
+
+        H1 = operator.to_dense()
+        return tf.add(i, 1),H1
+
+    H0 = tf.ones((1,1))
+    i,H2 = tf.while_loop(  c,logic,[i, H0  ])
+    H2 = tf.matmul(tf.matmul(H2, M), H2)
+
+    return H2
+
+
+#M = tf.ones((4,4),tf.float32)
+#M = tf.constant([[2, 3], [2, 5]], dtype=tf.float32)
+M = tf.linalg.band_part(tf.ones((4,4)), -1, 0)
+print(f'Hademard {hademard(M)}')
+
+{% endhighlight %}