ব্লগ । Blog
Jul 19
• Last one (E) becomes the root • Some immediate (backward) right ones ones (L A T) up until middle (you can choose) also becomes parents (downward right side parent) • – Then put some immediate ones (backward) ( N F ) as left children to come to root Then take alternate (K P) to …
Jul 19
From Pre-order Tree Traversal Output to Build the TreeFirst one becomes the root such as NSome immediate ones (D G) also becomes parents up until middle (you can choose) – left sub tree parens Then put some immediate ones (K P) as right children (in left subtree) to come to root Then take alternate (E …
Jul 19
From In-order Output to Build the Tree •Take In-order Traversal Output Data •And Build the Tree •Take the middle (N) or so as the root •Keep going/taking alternate left (nodeLabels) (K D) from there •Make those also roots/parents (left sub tree) •The last may be at the last left in this flow •Then from the …
May 20
Click on the images to see them clearly Example: Ref: https://docs.oracle.com/cd/B13789_01/server.101/b10759/statements_7004.htm
May 20
Click on Image to see them clearly Example: CREATE FUNCTION get_bal(acc_no IN NUMBER) RETURN NUMBER IS acc_bal NUMBER(11,2); BEGIN SELECT order_total INTO acc_bal FROM orders WHERE customer_id = acc_no; RETURN(acc_bal); END; / Ref: https://docs.oracle.com/en/database/oracle/oracle-database/12.2/lnpls/CREATE-FUNCTION-statement.html
May 20
Ref: https://docs.oracle.com/database/121/LNPLS/create_procedure.htm#LNPLS01373
May 19
#!/usr/bin/env python coding: utf-8 # 3rd Model: Deepgraph CNN: Stock Price Prediction using DeepGraphCNN Neural Networks. It includes GCN layers and CNN layers. I have added an MLP at the last layer to predict stock prices. # # Input graphs were created for spearman, Spearman, and Kendal Tau correlations/coefficients from historical stock prices. Also, another …
May 19
df_s_transpose_pearson = df_s_transpose.corr(method = ‘pearson’, numeric_only = True) df_s_transpose_pearson # Pearson Correlation Coefficient df_s_transpose_pearson = df_s_transpose.corr(method = ‘pearson’, numeric_only = True) df_s_transpose_pearson Pearson Correlation Coefficient based Adjacency Graph Matrix df_s_transpose_pearson[df_s_transpose_pearson >= 0.5] = 1 df_s_transpose_pearson[df_s_transpose_pearson < 0.5] = 0 df_s_transpose_pearson Create a Graph import networkx as nx Graph_pearson = nx.Graph(df_s_transpose_pearson) before the above step do: …
May 19
import libraries import os import pandas as pd import math Import Libraries for Graph, GNN (Graph Neural Network), and GCN (Graph Convolutional Network) import stellargraph as sg from stellargraph import StellarGraph from stellargraph.layer import DeepGraphCNN from stellargraph.mapper import FullBatchNodeGenerator from stellargraph.mapper import PaddedGraphGenerator from stellargraph.layer import GCN Machine Learning related library Imports (Tensorflow) from tensorflow.keras …
