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Update the weight matrix and the matrix product #175

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Update the weight matrix and the matrix product #175

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9 changes: 4 additions & 5 deletions ...bor_Methods/04_Computing_with_Mixed_Distance_Functions/04_mixed_distance_functions_knn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -49,7 +49,7 @@

## Create distance metric weight matrix weighted by standard deviation
weight_diagonal = x_vals.std(0)
weight_matrix = tf.cast(tf.diag(weight_diagonal), dtype=tf.float32)
weight_matrix = tf.cast(tf.expand_dims(weight_diagonal,1), dtype=tf.float32)

# Split the data into train and test sets
np.random.seed(13) # reproducible results
Expand All @@ -73,9 +73,8 @@
# Declare weighted distance metric
# Weighted L2 = sqrt((x-y)^T * A * (x-y))
subtraction_term = tf.subtract(x_data_train, tf.expand_dims(x_data_test,1))
first_product = tf.matmul(subtraction_term, tf.tile(tf.expand_dims(weight_matrix,0), [batch_size,1,1]))
second_product = tf.matmul(first_product, tf.transpose(subtraction_term, perm=[0,2,1]))
distance = tf.sqrt(tf.matrix_diag_part(second_product))
product = tf.matmul(tf.square(subtraction_term), tf.tile(tf.expand_dims(weight_matrix,0), [batch_size,1,1]))
distance = tf.sqrt(tf.squeeze(product,axis=2))

# Predict: Get min distance index (Nearest neighbor)
top_k_xvals, top_k_indices = tf.nn.top_k(tf.negative(distance), k=k)
Expand Down Expand Up @@ -113,4 +112,4 @@
plt.xlabel('Med Home Value in $1,000s')
plt.ylabel('Frequency')
plt.legend(loc='upper right')
plt.show()
plt.show()