diff --git a/api/__pycache__/app.cpython-310.pyc b/api/__pycache__/app.cpython-310.pyc
deleted file mode 100644
index a694bd589ce1602823bdfe06ffd3bb86cfc44283..0000000000000000000000000000000000000000
Binary files a/api/__pycache__/app.cpython-310.pyc and /dev/null differ
diff --git a/api/run.py b/api/run.py
index 78667ab11fd833ef291c9c160027b3878b816171..8a8319d4bec2f2b16d925633e644837019ee0453 100644
--- a/api/run.py
+++ b/api/run.py
@@ -1,72 +1,84 @@
-#====================================================
+# ====================================================
# Written By Don Gunasinha =
# Master in Data Science =
-#====================================================
+# ====================================================
-# libarary declare
+# libarary declare
from app import app
import pandas as pd
-import datetime as dt
+import datetime as dt
import numpy as np
import json
import time
+import math
+import sklearn.metrics
from sqlalchemy import create_engine
-from sklearn.preprocessing import MinMaxScaler
+from sklearn.preprocessing import MinMaxScaler
from tensorflow import keras
-from keras.models import Sequential
+from keras.models import Sequential
from keras.layers import Dense, LSTM, Dropout
+from statsmodels.tsa.seasonal import seasonal_decompose
+
from flask import Flask, jsonify, request, make_response
from flask import flash, request
from datetime import datetime, timedelta
-import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import seaborn as sns
+from matplotlib import pyplot
+import matplotlib.pyplot as plt
+from matplotlib.ticker import (MultipleLocator, FormatStrFormatter,
+ AutoMinorLocator)
+import matplotlib.dates as mdates
sns.set_theme(style="darkgrid")
-#=================================================
+# =================================================
-# Globally Declare data set
+# Globally Declare data set
-# Data Frames
-raw_data_set=pd.DataFrame()
-data_set =pd.DataFrame()
-index_data_set =pd.DataFrame()
-tranning_set=pd.DataFrame()
-result=pd.DataFrame()
-predictions_set=pd.DataFrame()
+# Data Frames
+raw_data_set = pd.DataFrame()
+data_set = pd.DataFrame()
+index_data_set = pd.DataFrame()
+tranning_set = pd.DataFrame()
+result = pd.DataFrame()
+predictions_set = pd.DataFrame()
+decompose_set = pd.DataFrame()
+model_settings = pd.DataFrame()
-# numpy Arrays
-data_arr_x=np.array([])
-data_arr_y=np.array([])
+# numpy Arrays
+data_arr_x = np.array([])
+data_arr_y = np.array([])
x_train = np.array([])
y_train = np.array([])
x_test = np.array([])
y_test = np.array([])
-predictions = np.array([])
+predictions = np.array([])
# Initialising the LSTM model with training
model = Sequential()
-feature_length = 100
-testing_records=50
+feature_length =0
+testing_records = 50
batch_size = 5
epochs = 10
-accuracy=0
-execute_time=0
-input_units=100
-hidden_layer_1=50
-hidden_layer_2=25
-output_units=1
+accuracy = 0
+execute_time = 0
+input_units = 100
+hidden_layer_1 = 67
+hidden_layer_2 = 34
+output_units = 1
-# Scaler
-scaler = MinMaxScaler()
+seetings_id=0
+
+# Scaler
+scaler = MinMaxScaler()
# MySQL database connection
@@ -74,412 +86,496 @@ db_connection_str = 'mysql+pymysql://root:@localhost/csct'
db_connection = create_engine(db_connection_str)
-#============================================
+# mesure perforemence
+rmse=0
+
+
+# ============================================
+
+# Support Functions definitions
-# Support Functions definitions
+def read_model_settings():
+ global model_settings
+ global feature_length
+ global batch_size
+ global epochs
+ global input_units
+ global hidden_layer_1
+ global hidden_layer_2
+ global output_units
+ global seetings_id
+ model_settings = pd.read_sql('select * from model_settings ORDER BY id DESC LIMIT 1', con=db_connection)
+ feature_length=model_settings['feature_length'].values[0]
+ batch_size=model_settings['batch_size'].values[0]
+ epochs=model_settings['epochs'].values[0]
+ input_units=model_settings['input_units'].values[0]
+ hidden_layer_1=model_settings['hidden_layer_1'].values[0]
+ hidden_layer_2=model_settings['hidden_layer_2'].values[0]
+ output_units=model_settings['output_units'].values[0]
+ seetings_id=model_settings['id'].values[0]
-# Read data from data base and re arrange data
+ return model_settings
-#---------------------------------------------
+
+
+# Read data from data base and re arrange data
+
+# ---------------------------------------------
def read_data_set():
- # Declare global variable
- global data_set
- global raw_data_set
- global index_data_set
+ # Declare global variable
+ global data_set
+ global raw_data_set
+ global index_data_set
+ global decompose_set
-
-
+ # Read data in to Data Frame
+ raw_data_set = pd.read_sql(
+ 'SELECT * FROM aw_product_demand', con=db_connection)
- # Read data in to Data Frame
- raw_data_set = pd.read_sql('SELECT * FROM aw_product_demand', con=db_connection)
+ # Validate Date
+ raw_data_set['Date'] = pd.to_datetime(raw_data_set['Date']).dt.date
+ raw_data_set['Order_Demand'] = raw_data_set['Order_Demand'].astype('int64')
- # Validate Date
- raw_data_set['Date']= pd.to_datetime(raw_data_set['Date']).dt.date
- raw_data_set['Order_Demand'] = raw_data_set['Order_Demand'].astype('int64')
+ # combine to single date
+ data_set = raw_data_set.groupby('Date')['Order_Demand'].sum().reset_index()
- #combine to single date
- data_set = raw_data_set.groupby('Date')['Order_Demand'].sum().reset_index()
-
- data_set.sort_values('Date', inplace=True)
- data_set['Date']=data_set['Date'].astype(str)
+ data_set.sort_values('Date', inplace=True)
+ data_set['Date'] = data_set['Date'].astype(str)
- # Create index data frame
- index_data_set=data_set
+ # Create index data frame
+ index_data_set = data_set
+ index_data_set = index_data_set.set_index(index_data_set['Date'])
+ index_data_set = index_data_set.drop('Date', axis=1)
- index_data_set=index_data_set.set_index(index_data_set['Date'])
+ #genarate decompose data set using seasonal_decompose
- return data_set
+ result = seasonal_decompose(index_data_set, model='multiplicative', extrapolate_trend='freq', period=100)
+
+ #push data in to pandas dataframe
+
+ decompose_set['Date'] = data_set['Date']
+ decompose_set['trend'] = result.trend.values
+ decompose_set['sesonal'] = result.seasonal.values
+ decompose_set['resid'] = result.resid.values
+ decompose_set['observed'] = result.observed.values
+
+ return data_set
+
+
+# -------------------------------------------------------------------------------
+def save_tranning_history(epochs, time_steps, batch_size, execute_time, rmse,mae,mape,accurecy):
+ global seetings_id
+
+ print(seetings_id)
+
+ id = db_connection.execute("INSERT INTO `trannings` ( `date_time`, `epochs`, `time_steps`, `batch_size`, `execute_time`, `rmse`,`mae`,`mape`,`accurecy`) VALUES ( CURRENT_TIMESTAMP, '" +
+ str(epochs)+"', '"+str(time_steps)+"', '"+str(batch_size)+"', '"+str(execute_time)+"', '"+str(rmse)+"', '"+str(mae)+"', '"+str(mape)+"','"+str(accurecy)+"')")
+
+ db_connection.execute("INSERT INTO `model_perforemence` ( `date_time`, `trannings_id`, `model_settings_id`) VALUES ( CURRENT_TIMESTAMP, '"+str(seetings_id)+"', '"+str(id.lastrowid)+"')")
+ print("Row Added = ", id.rowcount)
-#-------------------------------------------------------------------------------
-def save_tranning_history(epochs,time_steps,batch_size,execute_time,accuracy):
- id=db_connection.execute("INSERT INTO `trannings` ( `date_time`, `epochs`, `time_steps`, `batch_size`, `execute_time`, `accurecy`) VALUES ( CURRENT_TIMESTAMP, '"+str(epochs)+"', '"+str(time_steps)+"', '"+str(batch_size)+"', '"+str(execute_time)+"', '"+str(accuracy)+"')")
- print("Row Added = ",id.rowcount)
#-------------------------------------------------------------------------------
# Function to create x and y data
-def input_and_targert(data,feature_length):
-
- # Genarate Samples
- x_samples = list()
- y_samples = list()
- NumerOfRows=len(data)
-
- for i in range(feature_length , NumerOfRows , 1):
- x_sample = data[i-feature_length:i]
- y_sample = data[i]
- x_samples.append(x_sample)
- y_samples.append(y_sample)
-
- # Reshape the input as a 3D (Number of Samples,time steps,features)
- X = np.array(x_samples)
- X=X.reshape(X.shape[0],X.shape[1],1)
- print("\n____Input Data Shape :____")
- print(X.shape)
- # Reshape Target
- Y=np.array(y_samples)
- Y=Y.reshape(Y.shape[0],1)
- print("\n ______Target Data Shape :______")
- print(Y.shape)
-
- return X,Y
-#----------------------------------------------------------------------------
-# function to calculate prediction
+
+
+def input_and_targert(data, feature_length):
+
+ # Genarate Samples
+ x_samples = list()
+ y_samples = list()
+ NumerOfRows = len(data)
+
+ for i in range(feature_length, NumerOfRows, 1):
+ x_sample = data[i-feature_length:i]
+ y_sample = data[i]
+ x_samples.append(x_sample)
+ y_samples.append(y_sample)
+
+ # Reshape the input as a 3D (Number of Samples,time steps,features)
+ X = np.array(x_samples)
+ X = X.reshape(X.shape[0], X.shape[1], 1)
+ print("\n____Input Data Shape :____")
+ print(X.shape)
+ # Reshape Target
+ Y = np.array(y_samples)
+ Y = Y.reshape(Y.shape[0], 1)
+ print("\n ______Target Data Shape :______")
+ print(Y.shape)
+
+ return X, Y
+# ----------------------------------------------------------------------------
+# function to calculate prediction
+
def predict_given_date(data, date, feature_length):
- if date not in data.index:
- last_date=data.tail(1)
- data.loc[date]=0
- idx = data.index.get_loc(date)
- # close_col = data.iloc[:,1:2]
- # close_col = close_col.iloc[idx - feature_length : idx,:].values
- # close_col = np.expand_dims(scaler.transform(close_col) , axis = 0)
- # Prediction = model.predict(close_col)
- # Prediction=np.array(Prediction).reshape(-1, 1)
- # Prediction = scaler.inverse_transform(Prediction)
- return str(last_date)
+ if date not in data.index:
+ last_date = data.tail(1)
+ data.loc[date] = 0
+ idx = data.index.get_loc(date)
+ # close_col = data.iloc[:,1:2]
+ # close_col = close_col.iloc[idx - feature_length : idx,:].values
+ # close_col = np.expand_dims(scaler.transform(close_col) , axis = 0)
+ # Prediction = model.predict(close_col)
+ # Prediction=np.array(Prediction).reshape(-1, 1)
+ # Prediction = scaler.inverse_transform(Prediction)
+ return str(last_date)
-#------------------------------------------------------------------------------
+# ------------------------------------------------------------------------------
def one_step_ahade():
- global data_set
- global predictions_set
- global model
-
- last_values=data_set.tail(feature_length)['Order_Demand'].values
- last_values=np.array(last_values)
- print(last_values)
- last_values=scaler.transform(last_values.reshape(-1,1))
+ global data_set
+ global predictions_set
+ global model
- NumSamples=1
- TimeSteps=feature_length
- NumFeatures=1
- last_values=last_values.reshape(NumSamples,TimeSteps,NumFeatures)
- predicted_order_demand = model.predict(last_values)
- predicted_order_demand = scaler.inverse_transform(predicted_order_demand)
- predicted_order_demand=round(predicted_order_demand[0][0])
+ last_values = data_set.tail(feature_length)['Order_Demand'].values
+ last_values = np.array(last_values)
+ print(last_values)
+ last_values = scaler.transform(last_values.reshape(-1, 1))
- last_date=str(data_set.tail(1)["Date"].values[0])
- last_date = datetime.strptime(last_date, "%Y-%m-%d")
- last_date+= timedelta(days=1)
- new_date=datetime.strftime(last_date,"%Y-%m-%d")
+ NumSamples = 1
+ TimeSteps = feature_length
+ NumFeatures = 1
+ last_values = last_values.reshape(NumSamples, TimeSteps, NumFeatures)
+ predicted_order_demand = model.predict(last_values)
+ predicted_order_demand = scaler.inverse_transform(predicted_order_demand)
+ predicted_order_demand = round(predicted_order_demand[0][0])
- new_row={'Date':new_date,'Order_Demand':predicted_order_demand}
- data_set = data_set.append(new_row, ignore_index=True)
- predictions_set= predictions_set.append(new_row, ignore_index=True)
- print(predictions_set)
+ last_date = str(data_set.tail(1)["Date"].values[0])
+ last_date = datetime.strptime(last_date, "%Y-%m-%d")
+ last_date += timedelta(days=1)
+ new_date = datetime.strftime(last_date, "%Y-%m-%d")
+ new_row = {'Date': new_date, 'Order_Demand': predicted_order_demand}
+ data_set = data_set.append(new_row, ignore_index=True)
+ predictions_set = predictions_set.append(new_row, ignore_index=True)
+ print(predictions_set)
+# -----------------------------------------------------------------------------
-#------------------------------------------------------------------------------
# =========================== End of support Functions =================
-#============================= Main funtion ===================
+# ============================= Main funtion ===================
def setup():
- # Declare Global variables =================
+ # Declare Global variables =================
- global data_set
- global predictions
- global result
- global scaler
- global accuracy
- global execute_time
+ global data_set
+ global predictions
+ global result
+ global scaler
+ global accuracy
+ global execute_time
+ global rmse
- # Read Data from DataSet
- read_data_set()
+ # Read Data from DataSet
+ read_data_set()
+ print(data_set.describe())
- # Filter order Deamands array
- data_set["Order_Demand"]=pd.to_numeric(data_set.Order_Demand,errors='coerce')
- data = data_set.iloc[:,1:2]
- data = data.values
+ # Filter order Deamands array
+ data_set["Order_Demand"] = pd.to_numeric(
+ data_set.Order_Demand, errors='coerce')
+ data = data_set.iloc[:, 1:2]
+ print(data)
- ds = scaler.fit(data)
- scal_data=ds.transform(data)
+ print("--------------------------------------")
+ data = data.values
+ ds = scaler.fit(data)
+ scal_data = ds.transform(data)
- scal_data = scaler.fit_transform(data)
+ scal_data = scaler.fit_transform(data)
- x,y= input_and_targert(scal_data,feature_length)
-
- # Split the data into training and test
- x_train=x[:-testing_records]
- x_test=x[-testing_records:]
- y_train=y[:-testing_records]
- y_test=y[-testing_records:]
+ x, y = input_and_targert(scal_data, feature_length)
+ # Split the data into training and test
+ x_train = x[:-testing_records]
+ x_test = x[-testing_records:]
+ y_train = y[:-testing_records]
+ y_test = y[-testing_records:]
- print("\n___Tranning Data Shape___ ")
- print(x_train.shape)
- print(y_train.shape)
- print("\n___Testing Data Shape___ ")
- print(x_test.shape)
- print(y_test.shape)
+ print("\n___Tranning Data Shape___ ")
+ print(x_train.shape)
+ print(y_train.shape)
+ print("\n___Testing Data Shape___ ")
+ print(x_test.shape)
+ print(y_test.shape)
- for inp, out in zip(x_train[0:2], y_train[0:2]):
- print(inp,'--', out)
+ for inp, out in zip(x_train[0:2], y_train[0:2]):
+ print(inp, '--', out)
-
- # Defining Input shapes for LSTM
- time_steps=x_train.shape[1]
- features=x_train.shape[2]
- print("Number of TimeSteps:", time_steps)
- print("Number of Features:", features)
+ # Defining Input shapes for LSTM
+ time_steps = x_train.shape[1]
+ features = x_train.shape[2]
+ print("Number of TimeSteps:", time_steps)
+ print("Number of Features:", features)
+ # Add First LSTM Layer
+ model.add(LSTM(units=input_units, activation='relu', input_shape=(
+ time_steps, features), return_sequences=True))
+ # Adding the Second hidden layer and the LSTM layer
+ model.add(LSTM(units=hidden_layer_1, activation='relu',
+ input_shape=(time_steps, features), return_sequences=True))
- #Add First LSTM Layer
- model.add(LSTM(units = input_units, activation = 'relu', input_shape = (time_steps, features), return_sequences=True))
+ # Adding the Third hidden layer and the LSTM layer
+ model.add(LSTM(units=hidden_layer_2,
+ activation='relu', return_sequences=False))
- # Adding the Second hidden layer and the LSTM layer
- model.add(LSTM(units = hidden_layer_1, activation = 'relu', input_shape = (time_steps, features), return_sequences=True))
+ # Adding the output layer
+ model.add(Dense(units=output_units))
+ # Compiling model
+ model.compile(optimizer='adam', loss='mean_squared_error')
- # Adding the Third hidden layer and the LSTM layer
- model.add(LSTM(units = hidden_layer_2, activation = 'relu', return_sequences=False ))
+ print(model.input)
+ print(model.output)
- # Adding the output layer
- model.add(Dense(units = output_units))
- # Compiling model
- model.compile(optimizer = 'adam', loss = 'mean_squared_error')
+ print(model.summary())
- print(model.input)
- print(model.output)
+ # Measuring the time taken by the model to train
+ start_time = time.time()
- print(model.summary())
+ # Fitting the RNN to the Training set
+ model_history = model.fit(x_train, y_train, batch_size, epochs)
+ end_time = time.time()
+ execute_time = round((end_time-start_time)/60, 2)
+ print("__Total Time Taken: ", execute_time, ' Minutes___')
- # Measuring the time taken by the model to train
- start_time=time.time()
+ # Making predictions on test data
+ predicted = model.predict(x_test)
+ predicted = scaler.inverse_transform(predicted)
- # Fitting the RNN to the Training set
- model_history=model.fit(x_train, y_train, batch_size , epochs)
+ # Getting the original price values for testing data
+ orig = y_test
+ orig = scaler.inverse_transform(y_test)
- end_time=time.time()
- execute_time=round((end_time-start_time)/60,2)
- print("__Total Time Taken: ", execute_time, ' Minutes___')
+ # Accuracy of the predictions
+ accuracy = 100 - (100*(abs(orig-predicted)/orig)).mean()
+ mse = sklearn.metrics.mean_squared_error(orig, predicted)
+ rmse=math.sqrt(mse)
+ mae=sklearn.metrics.mean_absolute_error(orig, predicted)
+ mape=sklearn.metrics.mean_absolute_percentage_error(orig, predicted)
+ mape=mape
+ mape=round(mape,2)
+
+ accuracy = round(accuracy, 2)
+ print('Accuracy:', accuracy)
- # Making predictions on test data
- predicted_Price = model.predict(x_test)
- predicted_Price = scaler.inverse_transform(predicted_Price)
+ save_tranning_history(epochs, time_steps, batch_size,
+ execute_time, rmse,mae,mape,accuracy)
- # Getting the original price values for testing data
- orig=y_test
- orig=scaler.inverse_transform(y_test)
+ # Generating predictions on full data
+ TrainPredictions = scaler.inverse_transform(model.predict(x_train))
+ TestPredictions = scaler.inverse_transform(model.predict(x_test))
+ FullDataPredictions = np.append(TrainPredictions, TestPredictions)
- # Accuracy of the predictions
- accuracy=100 - (100*(abs(orig-predicted_Price)/orig)).mean()
- accuracy=round(accuracy,2)
- print('Accuracy:',accuracy)
+ # Save data to result data set
+
+ result["Date"] = data_set.iloc[time_steps:]["Date"]
+ result["Predictions"] = FullDataPredictions
+ result["OrderDemand"] = data[time_steps:]
+
+# ====================================================================
+# main function call
- save_tranning_history(epochs,time_steps,batch_size,execute_time,accuracy)
+read_model_settings()
- # Generating predictions on full data
- TrainPredictions=scaler.inverse_transform(model.predict(x_train))
- TestPredictions=scaler.inverse_transform(model.predict(x_test))
- FullDataPredictions=np.append(TrainPredictions, TestPredictions)
+setup()
- # Save data to result data set
- result["Date"]=data_set.iloc[time_steps:]["Date"]
- result["Predictions"]=FullDataPredictions
- result["OrderDemand"]=data[time_steps:]
-
# ====================================================================
-# main function call
-setup()
+# end points declaration
+
+@app.route("/plot_result", methods=['GET'])
+def plot_result():
+
+ # plotting the full data
+ plt.plot(result["Predictions"], color='blue',
+ label='Predicted Order Demand')
+ plt.plot(result["OrderDemand"], color='lightblue', label='Original Price')
+ plt.title('Order Demand Predictions')
+ plt.xlabel(' Date')
+ plt.ylabel('Order Demand')
+ plt.legend()
+ fig = plt.gcf()
+ fig.set_figwidth(20)
+ fig.set_figheight(8)
+ plt.show()
+
+ return "plot complete"
-#====================================================================
+@app.route('/decompose', methods=['GET'])
+def decompose():
+ return decompose_set.to_json(orient='records')
-# end points declaration
-@app.route("/predict_one_step",methods=['GET'])
+@app.route('/decompose_plot_resid', methods=['GET'])
+def decompose_plot():
+ result = seasonal_decompose(
+ index_data_set, model='multiplicative', extrapolate_trend='freq', period=100)
+
+ ax = sns.lineplot(data=result.resid)
+
+ ax.xaxis.set_major_locator(mdates.DayLocator(interval=30))
+ plt.gcf().autofmt_xdate()
+
+ plt.show()
+ return decompose_set.to_json(orient='records')
+
+
+@app.route("/predict_one_step", methods=['GET'])
def predict_one_step():
- one_step_ahade()
- steps=str(len(predictions_set))
- return steps
+ one_step_ahade()
+ steps = str(len(predictions_set))
+ return steps
+
-@app.route("/predict_result",methods=['GET'])
+@app.route("/predict_result", methods=['GET'])
def predicpredict_result():
global predictions_set
return predictions_set.to_json(orient='records')
-
-
-
-@app.route("/plot_order-demands",methods=['GET'])
+@app.route("/plot_order-demands", methods=['GET'])
def plot_order_demands_total():
- global raw_data_set
- sns.lineplot(x='Date', y='Order_Demand',hue='Warehouse', data=raw_data_set)
- plt.figure(figsize=(20, 7))
- plt.xticks(rotation=15)
- plt.gcf().autofmt_xdate()
+ global raw_data_set
+ sns.lineplot(x='Date', y='Order_Demand',
+ hue='Warehouse', data=raw_data_set)
+ plt.figure(figsize=(20, 7))
+ plt.xticks(rotation=15)
+ plt.gcf().autofmt_xdate()
- plt.legend()
- plt.show()
+ plt.legend()
+ plt.show()
- return "Order demand"
+ return "Order demand"
-@app.route("/plot_order_demand_total",methods=['GET'])
+@app.route("/plot_order_demand_total", methods=['GET'])
def plot_order_demands():
- global data_set
- import matplotlib.pyplot as plt
- from matplotlib.ticker import (MultipleLocator, FormatStrFormatter,
- AutoMinorLocator)
- import matplotlib.dates as mdates
-
- dtFmt = mdates.DateFormatter('%Y-%b') # define the formatting
+ global data_set
- sns.lineplot(x='Date', y='Order_Demand', data=data_set)
- plt.figure(figsize=(20, 7))
- plt.gca().xaxis.set_major_formatter(dtFmt)
- # show every 12th tick on x axes
- plt.gca().xaxis.set_major_locator(mdates.MonthLocator(interval=1))
- plt.xticks(rotation=90, fontweight='light', fontsize='x-small',)
- plt.legend()
- plt.show()
+ dtFmt = mdates.DateFormatter('%Y-%b') # define the formatting
- return "Order demand"
+ sns.lineplot(x='Date', y='Order_Demand', data=data_set)
+ plt.figure(figsize=(20, 7))
+ plt.gca().xaxis.set_major_formatter(dtFmt)
+ # show every 12th tick on x axes
+ plt.gca().xaxis.set_major_locator(mdates.MonthLocator(interval=1))
+ plt.xticks(rotation=90, fontweight='light', fontsize='x-small',)
+ plt.legend()
+ plt.show()
+ return "Order demand"
-@app.route("/products",methods=['GET'])
+@app.route("/products", methods=['GET'])
def products():
- global raw_data_set
- result_array =raw_data_set['Product_code'].unique()
- return result_array.tolist()
+ global raw_data_set
+ result_array = raw_data_set['Product_code'].unique()
+ return result_array.tolist()
-@app.route("/date_range",methods=['GET'])
+@app.route("/date_range", methods=['GET'])
def date_range():
- result_array =data_set['Date'].unique()
- return result_array.tolist()
+ result_array = data_set['Date'].unique()
+ return result_array.tolist()
-@app.route("/warehouse",methods=['GET'])
-
+@app.route("/warehouse", methods=['GET'])
def warehouse():
- global raw_data_set
- warehouse_array =raw_data_set['Warehouse'].unique()
- return warehouse_array.tolist()
+ global raw_data_set
+ warehouse_array = raw_data_set['Warehouse'].unique()
+ return warehouse_array.tolist()
-@app.route("/row_data",methods=['GET'])
+@app.route("/row_data", methods=['GET'])
def row_data():
- global raw_data_set
- return raw_data_set.to_json(orient='records')
-
+ global raw_data_set
+ return raw_data_set.to_json(orient='records')
-
-@app.route("/validation",methods=['GET'])
+@app.route("/forecast_data", methods=['GET'])
def validation():
- global result
- result=result.set_index(result['Date'])
- return result.to_json(orient='records')
+ global result
+ result = result.set_index(result['Date'])
+ return result.to_json(orient='records')
+
-@app.route("/forecast_to_date",methods=["POST"])
+@app.route("/forecast_to_date", methods=["POST"])
def forecast_to_date():
- global data_set
- global scaler
- global index_data_set
- global model
-
- # read incomming json data
- data=request.get_json()
- date=data['date']
- new_date=datetime.strptime(date, "%Y-%m-%d").date()
- new_date=new_date - timedelta(days=(feature_length-1))
- new_date=new_date.strftime("%Y-%m-%d")
- print(new_date)
-
-
- result=predict_given_date(index_data_set,new_date, feature_length)
- # df=pd.DataFrame()
- # # df=pd.DataFrame(data=result,columns=["Prediction"])
- # df['Date']=pd.date_range(start=new_date,periods=feature_length)
- # df=df.loc[::-1]
- # df['Prediction']=result
-
-
- # df['Date']= pd.to_datetime(df['Date']).dt.date
- # df.sort_values('Date', inplace=True)
- # df['Date']=df['Date'].astype(str)
- # df=df.set_index(df['Date'])
- # df=df.tail(1)
-
- return result
-
-@app.route('/get_accuracy',method=["POST"])
-def get_accuracy():
- # model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
- # history = model.fit(X, Y, epochs=100)
- # print(history.history['loss'])
- # print(history.history['accuracy'])
- return "accuracy"
-
-@app.route("/forecast_to_range",methods=["POST"])
+ global data_set
+ global scaler
+ global index_data_set
+ global model
+
+ # read incomming json data
+ data = request.get_json()
+ date = data['date']
+ new_date = datetime.strptime(date, "%Y-%m-%d").date()
+ new_date = new_date - timedelta(days=(feature_length-1))
+ new_date = new_date.strftime("%Y-%m-%d")
+ print(new_date)
+
+ result = predict_given_date(index_data_set, new_date, feature_length)
+ # df=pd.DataFrame()
+ # # df=pd.DataFrame(data=result,columns=["Prediction"])
+ # df['Date']=pd.date_range(start=new_date,periods=feature_length)
+ # df=df.loc[::-1]
+ # df['Prediction']=result
+
+ # df['Date']= pd.to_datetime(df['Date']).dt.date
+ # df.sort_values('Date', inplace=True)
+ # df['Date']=df['Date'].astype(str)
+ # df=df.set_index(df['Date'])
+ # df=df.tail(1)
+
+ return result
+
+
+@app.route("/forecast_to_range", methods=["POST"])
def forecast_to_range():
- global data_set
- global scaler
- global index_data_set
- global model
-
- index_data_set=data_set
-
- index_data_set=index_data_set.set_index(index_data_set['Date'])
-
- # read incomming json data
- data=request.get_json()
- date=data['date']
- new_date=datetime.strptime(date, "%Y-%m-%d").date()
- new_date=new_date - timedelta(days=(feature_length-1))
- new_date=new_date.strftime("%Y-%m-%d")
-
- result=predict_given_date(index_data_set,new_date, feature_length)
- df=pd.DataFrame()
- # df=pd.DataFrame(data=result,columns=["Prediction"])
- df['Date']=pd.date_range(start=new_date,periods=feature_length)
- df=df.loc[::-1]
- df['Prediction']=result
- df['Date']= pd.to_datetime(df['Date']).dt.date
- df.sort_values('Date', inplace=True)
- df['Date']=df['Date'].astype(str)
- df=df.set_index(df['Date'])
- return df.to_json(orient='records')
+ global data_set
+ global scaler
+ global index_data_set
+ global model
+
+ index_data_set = data_set
+
+ index_data_set = index_data_set.set_index(index_data_set['Date'])
+
+ # read incomming json data
+ data = request.get_json()
+ date = data['date']
+ new_date = datetime.strptime(date, "%Y-%m-%d").date()
+ new_date = new_date - timedelta(days=(feature_length-1))
+ new_date = new_date.strftime("%Y-%m-%d")
+
+ result = predict_given_date(index_data_set, new_date, feature_length)
+ df = pd.DataFrame()
+ # df=pd.DataFrame(data=result,columns=["Prediction"])
+ df['Date'] = pd.date_range(start=new_date, periods=feature_length)
+ df = df.loc[::-1]
+ df['Prediction'] = result
+ df['Date'] = pd.to_datetime(df['Date']).dt.date
+ df.sort_values('Date', inplace=True)
+ df['Date'] = df['Date'].astype(str)
+ df = df.set_index(df['Date'])
+ return df.to_json(orient='records')
if __name__ == "__main__":
- app.run()
\ No newline at end of file
+ app.run()
diff --git a/dash_board.php b/dash_board.php
index 3942082e97af264b700324edad8658af16eba617..f8c22ed1fefa202af4d6e352f557d7b8ebcfa44d 100644
--- a/dash_board.php
+++ b/dash_board.php
@@ -97,8 +97,70 @@
option && myChart.setOption(option);
</script>
-
- <script src="dist/js/chart2.js" type="text/javascript"> </script>
+
+ <script type="text/javascript">
+ var dom = document.getElementById('chart2');
+ var myChart = echarts.init(dom, null, {
+ renderer: 'canvas',
+ useDirtyRect: false
+ });
+ var app = {};
+
+ var option2;
+
+ option2 = {
+ tooltip: {
+ trigger: 'item'
+ },
+ legend: {
+ bottom: '5%',
+ left: 'center'
+ },
+ series: [
+ {
+ name: 'Access From',
+ type: 'pie',
+ radius: ['40%', '70%'],
+ avoidLabelOverlap: false,
+ itemStyle: {
+ borderRadius: 10,
+ borderColor: '#fff',
+ borderWidth: 2
+ },
+ label: {
+ show: false,
+ position: 'center'
+ },
+ emphasis: {
+ label: {
+ show: true,
+ fontSize: 40,
+ fontWeight: 'bold'
+ }
+ },
+ labelLine: {
+ show: false
+ },
+ data: [
+ { value: <?=$total_order_demands?>, name: 'Order demands' },
+ { value: <?=$total_predection?>, name: 'Predictions' },
+ { value: <?=$total_error?>, name: 'Error' },
+ ]
+ }
+ ]
+ };
+
+
+ if (option2 && typeof option2 === 'object') {
+ myChart.setOption(option2);
+ }
+
+ window.addEventListener('resize', myChart.resize);
+
+ </script>
+
+
+
<script src="dist/js/chart3.js" type="text/javascript"> </script>
diff --git a/decompose.php b/decompose.php
new file mode 100644
index 0000000000000000000000000000000000000000..1d8b1c12e192bea6f7192dc4dc7ecbf282efc4b9
--- /dev/null
+++ b/decompose.php
@@ -0,0 +1,178 @@
+<div class="content-wrapper">
+
+ <?php
+ $title = "Analysis Result";
+ $t1 = "Home";
+ $t2 = "Decomposition";
+
+
+ include_once './page_header.php';?>
+ <!-- Main content -->
+ <div class="content">
+ <div class="container-fluid">
+
+ <?php include_once './data/decompose_data.php';?>
+
+
+
+ <div class="row" style="padding: 10px;">
+
+ <div class="col-md-12">
+
+ <div style="width:full; height:400px;" id="chart1"></div>
+
+ </div>
+
+ <div class="col-md-12">
+
+ <div style="width:full; height:400px;" id="chart2"></div>
+
+ </div>
+
+ <div class="col-md-12">
+
+ <div style="width:full; height:400px;" id="chart3"></div>
+
+ </div>
+
+
+ </div>
+
+ <script>
+ var chartDom = document.getElementById('chart1');
+ var myChart = echarts.init(chartDom);
+ var option;
+
+ option = {
+ title: {
+ text: 'Trend'
+ },
+ tooltip: {
+ trigger: 'axis'
+ },
+ legend: {
+ data: ['Trend']
+ },
+ grid: {
+ left: '3%',
+ right: '4%',
+ bottom: '3%',
+ containLabel: true
+ },
+ toolbox: {
+ feature: {
+ saveAsImage: {}
+ }
+ },
+ xAxis: {
+ type: 'category',
+ data: [<?=$dates?>]
+ },
+ yAxis: {
+ type: 'value'
+ },
+ series: [{
+ data: [<?=$trend?>],
+ type: 'line',
+ smooth: true
+ }]
+ };
+
+ option && myChart.setOption(option);
+ </script>
+
+ <script>
+ var chartDom = document.getElementById('chart2');
+ var myChart2 = echarts.init(chartDom);
+ var option2;
+
+ option2 = {
+ title: {
+ text: 'Sesonal'
+ },
+ tooltip: {
+ trigger: 'axis'
+ },
+ legend: {
+ data: ['Sesonal']
+ },
+ grid: {
+ left: '3%',
+ right: '4%',
+ bottom: '3%',
+ containLabel: true
+ },
+ toolbox: {
+ feature: {
+ saveAsImage: {}
+ }
+ },
+ xAxis: {
+ type: 'category',
+ data: [<?=$dates?>]
+ },
+ yAxis: {
+ type: 'value'
+ },
+ series: [{
+ data: [<?=$sesonal?>],
+ type: 'line',
+ smooth: true
+ }]
+ };
+
+ option2 && myChart2.setOption(option2);
+ </script>
+
+
+<script>
+ var chartDom = document.getElementById('chart3');
+ var myChart3 = echarts.init(chartDom);
+ var option3;
+
+ option3 = {
+ title: {
+ text: 'Resid'
+ },
+ tooltip: {
+ trigger: 'axis'
+ },
+ legend: {
+ data: ['Resid']
+ },
+ grid: {
+ left: '3%',
+ right: '4%',
+ bottom: '3%',
+ containLabel: true
+ },
+ toolbox: {
+ feature: {
+ saveAsImage: {}
+ }
+ },
+ xAxis: {
+ type: 'category',
+ data: [<?=$dates?>]
+ },
+ yAxis: {
+ type: 'value'
+ },
+ series: [{
+ data: [<?=$resid?>],
+ type: 'line',
+ smooth: true
+ }]
+ };
+
+ option3 && myChart3.setOption(option3);
+ </script>
+
+
+ <!-- /.row -->
+ </div>
+ <!-- /.container-fluid -->
+ </div>
+ <!-- /.content -->
+</div>
+<!-- /.content-wrapper -->
\ No newline at end of file
diff --git a/forecast_list.php b/forecast_list.php
new file mode 100644
index 0000000000000000000000000000000000000000..a0a7cd383b3a064210437af7a194936cb5fe8046
--- /dev/null
+++ b/forecast_list.php
@@ -0,0 +1,120 @@
+<?php
+include_once './data/forecast_data.php';
+include_once('header.php');
+
+?>
+
+
+<body class="hold-transition sidebar-mini">
+ <div class="wrapper">
+ <!-- Navbar -->
+
+ <?php include_once('./nav_bar.php'); ?>
+ <!-- Main Sidebar Container -->
+ <?php include_once('./side_bar.php'); ?>
+
+
+ <!-- Content Wrapper. Contains page content -->
+ <div class="content-wrapper">
+ <!-- Content Header (Page header) -->
+
+ <?php
+ $title = "Model Settings List ";
+ $t1="Model";
+ $t2= " Settings List";
+
+
+
+ ?>
+
+ <?php include_once('page_header.php')?>
+
+ <!-- Main content -->
+ <section class="content">
+ <div class="row">
+ <div class="col-12">
+
+ <div class="card">
+
+ <div class="card-header">
+ <h3 class="card-title">
+ <button type="button" class="btn btn-block btn-outline-secondary"
+ onclick="location.href = 'settings.php';">Add New Record</button>
+
+ </h3>
+ </div>
+
+ <!-- /.card-header -->
+ <div class="card-body">
+ <table id="example23"
+ class="display nowrap table table-hover table-striped table-bordered"
+ cellspacing="0" width="100%">
+ <thead>
+ <tr>
+ <th>#</th>
+ <th>Date</th>
+ <th>Forecast Order Demand</th>
+ <th>Epochs</th>
+ <th>Input Units</th>
+ <th>Hidden Layer 1</th>
+ <th>Hiddden Layer 2</th>
+ <th>Output Layer</th>
+
+
+
+ </tr>
+ </thead>
+ <tfoot>
+ <tr>
+ <th>#</th>
+ <th>Feture Length</th>
+ <th>batch size</th>
+ <th>Epochs</th>
+ <th>Input Units</th>
+ <th>Hidden Layer 1</th>
+ <th>Hiddden Layer 2</th>
+ <th>Output Layer</th>
+ </tr>
+ </tfoot>
+ <tbody>
+ <?php
+ $i = 1;
+ while ($row = mysqli_fetch_assoc($result)) {
+
+
+ ?>
+ <tr>
+ <td><?php echo $i++; ?></td>
+
+ <td><?= $row['feature_length'] ?></td>
+ <td><?= $row['batch_size'] ?></td>
+ <td><?= $row['epochs'] ?></td>
+ <td><?= $row['input_units'] ?></td>
+ <td><?= $row['hidden_layer_1'] ?></td>
+ <td><?= $row['hidden_layer_2'] ?></td>
+ <td><?= $row['output_units'] ?></td>
+ </tr>
+ <?php } ?>
+ </tbody>
+ </table>
+ </div>
+ <!-- /.card-body -->
+ </div>
+ <!-- /.card -->
+ </div>
+ <!-- /.col -->
+ </div>
+ <!-- /.row -->
+ </section>
+ <!-- /.content -->
+ </div>
+
+ <?php include_once('control_side_bar.php');?>
+ <!-- /.content-wrapper -->
+ <?php include_once('footer.php');?>
+
+ </div>
+ <?php include_once('footer_script.php');?>
+</body>
+
+</html>
\ No newline at end of file
diff --git a/inc/function.php b/inc/function.php
index e5b8515f7cd49b8b1f38c18e31a781479c436574..e1c54d9b7226cf4a199736b4fb50b2d0d69f360c 100644
--- a/inc/function.php
+++ b/inc/function.php
@@ -13,6 +13,11 @@ function get_accurecy($conn){
return $res['accurecy'];
+}
+
+function get_layer_1_nodes(){
+
+
}
function get_epochs($conn){
diff --git a/index.php b/index.php
index 457eb671722121219bf7e794da0d9812127d2dea..25cb3b3314b6242e772935dc35b739278991d638 100644
--- a/index.php
+++ b/index.php
@@ -3,12 +3,6 @@
include_once('inc/conn.php');
include_once('header.php');
-
-
-
-
-
-
?>
<body class="hold-transition sidebar-mini">
diff --git a/output.png b/output.png
new file mode 100644
index 0000000000000000000000000000000000000000..9f7e2eb4aa8555cc8df98bcee38001670dc8f135
Binary files /dev/null and b/output.png differ
diff --git a/page_decompose.php b/page_decompose.php
new file mode 100644
index 0000000000000000000000000000000000000000..c8aa7bf8813e1f6fa2a5cf0c6af61deefdd861c5
--- /dev/null
+++ b/page_decompose.php
@@ -0,0 +1,26 @@
+<?php
+include_once('inc/conn.php');
+include_once('header.php');
+
+?>
+
+<body class="hold-transition sidebar-mini">
+ <div class="wrapper">
+
+
+
+ <?php include_once('./nav_bar.php'); ?>
+ <!-- Main Sidebar Container -->
+ <?php include_once('./side_bar.php'); ?>
+
+ <?php include_once('decompose.php');?>
+
+ <?php include_once('control_side_bar.php');?>
+
+ <?php include_once('footer.php');?>
+ </div>
+
+ <?php include_once('footer_script.php');?>
+</body>
+
+</html>
\ No newline at end of file
diff --git a/perforemence_list.php b/perforemence_list.php
new file mode 100644
index 0000000000000000000000000000000000000000..e3ac5bf47426dd6fb1e2d6af133ef7dc015180b6
--- /dev/null
+++ b/perforemence_list.php
@@ -0,0 +1,132 @@
+<?php
+include_once './data/data_mp.php';
+include_once('header.php');
+
+
+?>
+
+
+
+
+<body class="hold-transition sidebar-mini">
+ <div class="wrapper">
+ <!-- Navbar -->
+
+ <?php include_once('./nav_bar.php'); ?>
+ <!-- Main Sidebar Container -->
+ <?php include_once('./side_bar.php'); ?>
+
+
+ <!-- Content Wrapper. Contains page content -->
+ <div class="content-wrapper">
+ <!-- Content Header (Page header) -->
+
+ <?php
+ $title = "Model Settings List ";
+ $t1="Model";
+ $t2= " Settings List";
+
+
+
+ ?>
+
+ <?php include_once('page_header.php')?>
+
+ <!-- Main content -->
+ <section class="content">
+ <div class="row">
+ <div class="col-12">
+
+ <div class="card">
+
+ <div class="card-header">
+ <h3 class="card-title">
+ <button type="button" class="btn btn-block btn-outline-secondary"
+ onclick="location.href = 'settings.php';">Add New Record</button>
+
+ </h3>
+ </div>
+
+ <!-- /.card-header -->
+ <div class="card-body">
+ <table id="example23"
+ class="display nowrap table table-hover table-striped table-bordered"
+ cellspacing="0" width="100%">
+ <thead>
+ <tr>
+ <th>#</th>
+ <th>Input Nodes</th>
+ <th>Hidden layer 1 nodes</th>
+ <th>Hidden layer 2 nodes</th>
+ <th>output nodes</th>
+ <th>Epochs</th>
+ <th>RMSE</th>
+ <th>MAE</th>
+ <th>MAPE</th>
+ <th>Accurecy %</th>
+
+
+
+
+ </tr>
+ </thead>
+ <tfoot>
+ <tr>
+ <th>#</th>
+ <th>Input Nodes</th>
+ <th>Hidden layer 1 nodes</th>
+ <th>Hidden layer 2 nodes</th>
+ <th>output nodes</th>
+ <th>Epochs</th>
+ <th>RMSE</th>
+ <th>MAE</th>
+ <th>MAPE</th>
+ <th>Accurecy %</th>
+
+ </tr>
+ </tfoot>
+ <tbody>
+ <?php
+ $i = 1;
+ while ($row = mysqli_fetch_assoc($result)) {
+
+
+ ?>
+ <tr>
+ <td><?php echo $i++; ?></td>
+
+ <td><?= $row['input_units'] ?></td>
+ <td><?= $row['hidden_layer_1'] ?></td>
+ <td><?= $row['hidden_layer_2'] ?></td>
+ <td><?= $row['output_units'] ?></td>
+ <td><?= $row['epochs'] ?></td>
+ <td><?= round($row['rmse'],3) ?></td>
+ <td><?= round($row['mae'],3) ?></td>
+ <td><?= $row['mape'] ?></td>
+ <td><?= $row['accurecy'] ?></td>
+
+ </tr>
+ <?php } ?>
+ </tbody>
+ </table>
+ </div>
+ <!-- /.card-body -->
+ </div>
+ <!-- /.card -->
+ </div>
+ <!-- /.col -->
+ </div>
+ <!-- /.row -->
+ </section>
+ <!-- /.content -->
+ </div>
+
+ <?php include_once('control_side_bar.php');?>
+ <!-- /.content-wrapper -->
+ <?php include_once('footer.php');?>
+
+ </div>
+ <?php include_once('footer_script.php');?>
+</body>
+
+</html>
\ No newline at end of file
diff --git a/settings.php b/settings.php
index b82369805d4821ac5a9a9350b441228179cfe9d9..4dbd154ff5d6138791f4960812129b0b52d70084 100644
--- a/settings.php
+++ b/settings.php
@@ -47,22 +47,75 @@ include_once('header.php');
<div class="form-row">
<div class="col-lg-6 col-md-6 form-group">
- <lable>Number of Nodes for Layer 1:</lable>
+ <lable>Feature Length:</lable>
- <input type="text" class="form-control" name="layer1_nodes"
+ <input type="text" class="form-control" name="feture_length"
placeholder="0">
</div>
<div class="col-lg-6 col-md-6 form-group">
- <lable>Number of Nodes for Layer 2:</lable>
-
- <input type="text" class="form-control" name="layer2_nodes"
+ <lable>Batch Size:</lable>
+
+ <input type="text" class="form-control" name="batch_size"
+ placeholder="0">
+
+ </div>
+
+ <div class="col-lg-6 col-md-6 form-group">
+ <lable>Epochs:</lable>
+
+ <input type="text" class="form-control" name="epochs"
+ placeholder="0">
+
+ </div>
+
+ <div class="col-lg-6 col-md-6 form-group">
+ <lable>Input Layer:</lable>
+
+ <input type="text" class="form-control" name="input_units"
placeholder="0">
</div>
+ <div class="col-lg-6 col-md-6 form-group">
+ <lable>Hidden Layer 1:</lable>
+
+ <input type="text" class="form-control" name="hidden_layer_1"
+ placeholder="0">
+ </div>
+ <div class="col-lg-6 col-md-6 form-group">
+ <lable>Hidden Layer 2:</lable>
+
+ <input type="text" class="form-control" name="hidden_layer_2"
+ placeholder="0">
+ </div>
+
+ <div class="col-lg-6 col-md-6 form-group">
+ <lable>Output Layer:</lable>
+
+ <input type="text" class="form-control" name="output_layer"
+ placeholder="0">
+
+ </div>
+
+
+
+
+
+ </div>
+
+ <div class="form-row">
+
+ <div class="col-md-6">
+
+ </div>
+
+ <div class="col-md-6 ">
+ <input type="submit" class="btn btn-outline-success float-right" value="Add New">
+ </div>
+
</div>
diff --git a/settings_list.php b/settings_list.php
new file mode 100644
index 0000000000000000000000000000000000000000..4692285eb81b0ea6fafe8a304a501782b93bcbdb
--- /dev/null
+++ b/settings_list.php
@@ -0,0 +1,120 @@
+<?php
+include_once './data/model_settings_list.php';
+include_once('header.php');
+
+?>
+
+
+<body class="hold-transition sidebar-mini">
+ <div class="wrapper">
+ <!-- Navbar -->
+
+ <?php include_once('./nav_bar.php'); ?>
+ <!-- Main Sidebar Container -->
+ <?php include_once('./side_bar.php'); ?>
+
+
+ <!-- Content Wrapper. Contains page content -->
+ <div class="content-wrapper">
+ <!-- Content Header (Page header) -->
+
+ <?php
+ $title = "Model Settings List ";
+ $t1="Model";
+ $t2= " Settings List";
+
+
+
+ ?>
+
+ <?php include_once('page_header.php')?>
+
+ <!-- Main content -->
+ <section class="content">
+ <div class="row">
+ <div class="col-12">
+
+ <div class="card">
+
+ <div class="card-header">
+ <h3 class="card-title">
+ <button type="button" class="btn btn-block btn-outline-secondary"
+ onclick="location.href = 'settings.php';">Add New Record</button>
+
+ </h3>
+ </div>
+
+ <!-- /.card-header -->
+ <div class="card-body">
+ <table id="example23"
+ class="display nowrap table table-hover table-striped table-bordered"
+ cellspacing="0" width="100%">
+ <thead>
+ <tr>
+ <th>#</th>
+ <th>Feture Length</th>
+ <th>batch size</th>
+ <th>Epochs</th>
+ <th>Input Units</th>
+ <th>Hidden Layer 1</th>
+ <th>Hiddden Layer 2</th>
+ <th>Output Layer</th>
+
+
+
+ </tr>
+ </thead>
+ <tfoot>
+ <tr>
+ <th>#</th>
+ <th>Feture Length</th>
+ <th>batch size</th>
+ <th>Epochs</th>
+ <th>Input Units</th>
+ <th>Hidden Layer 1</th>
+ <th>Hiddden Layer 2</th>
+ <th>Output Layer</th>
+ </tr>
+ </tfoot>
+ <tbody>
+ <?php
+ $i = 1;
+ while ($row = mysqli_fetch_assoc($result)) {
+
+
+ ?>
+ <tr>
+ <td><?php echo $i++; ?></td>
+
+ <td><?= $row['feature_length'] ?></td>
+ <td><?= $row['batch_size'] ?></td>
+ <td><?= $row['epochs'] ?></td>
+ <td><?= $row['input_units'] ?></td>
+ <td><?= $row['hidden_layer_1'] ?></td>
+ <td><?= $row['hidden_layer_2'] ?></td>
+ <td><?= $row['output_units'] ?></td>
+ </tr>
+ <?php } ?>
+ </tbody>
+ </table>
+ </div>
+ <!-- /.card-body -->
+ </div>
+ <!-- /.card -->
+ </div>
+ <!-- /.col -->
+ </div>
+ <!-- /.row -->
+ </section>
+ <!-- /.content -->
+ </div>
+
+ <?php include_once('control_side_bar.php');?>
+ <!-- /.content-wrapper -->
+ <?php include_once('footer.php');?>
+
+ </div>
+ <?php include_once('footer_script.php');?>
+</body>
+
+</html>
\ No newline at end of file
diff --git a/side_bar.php b/side_bar.php
index 99520a4b849505582b4af939f648616b56f34423..041f873b176cc83090f1a9a556172f727144345f 100644
--- a/side_bar.php
+++ b/side_bar.php
@@ -46,11 +46,11 @@
<li class="nav-item">
<a href="run.php" class="nav-link">
<i class="fa fa-play nav-icon"></i>
- <p>Run Model</p>
+ <p>Model History</p>
</a>
</li>
<li class="nav-item">
- <a href="settings.php" class="nav-link">
+ <a href="settings_list.php" class="nav-link">
<i class="fa fa-cog nav-icon"></i>
<p>Model Settings</p>
</a>
@@ -73,37 +73,44 @@
<p>Get the forecast</p>
</a>
</li>
+
+ <li class="nav-item">
+ <a href="page_forecast.php" class="nav-link">
+ <i class="fas fa-list nav-icon"></i>
+ <p>List Forecast</p>
+ </a>
+ </li>
</ul>
</li>
<li class="nav-header">Data Analytics</li>
<li class="nav-item">
- <a href="products.php" class="nav-link">
- <i class="nav-icon fas fa-calendar-alt"></i>
+ <a href="page_decompose.php" class="nav-link">
+ <i class="nav-icon far fa-chart-bar"></i>
<p>
- Products
+ Time Series
</p>
</a>
</li>
<li class="nav-item">
- <a href="pages/gallery.html" class="nav-link">
- <i class="nav-icon far fa-image"></i>
+ <a href="perforemence_list.php" class="nav-link">
+ <i class="nav-icon fa-solid fa-dumbbell"></i>
<p>
- Warehouse
+ Model Perforemence
</p>
</a>
</li>
- <li class="nav-item">
+ <!-- <li class="nav-item">
<a href="category.php" class="nav-link">
<i class="nav-icon far fa-image"></i>
<p>
Category
</p>
</a>
- </li>
+ </li> -->
</ul>