working on demo

eth/keys/out_private.json

@@ -1 +1 @@
-"7001944839470927745373394821470350369266097788845460368251493193132763972406"
+"21127377611760855151397025938040417124468957529580509010001250098001910171200"

eth/keys/out_public.json

@@ -1,4 +1,4 @@
 [
-  "3696244767974077653455679795135848733601425960305578671455690036664496274007",
-  "10438300322976132768889289517836059848150371544640472911377544330755492363717"
+  "19517200380561602233013947304080897707587995089006775240619814703482168364893",
+  "10758991262778566768807205545860719510109920355369254969648901060785189870847"
 ]

eth/package.json

@@ -35,6 +35,7 @@
   },
   "scripts": {
     "compile": "npx hardhat compile",
-    "deploy-localhost": "npx hardhat run --network localhost scripts/deploy.js"
+    "deploy-localhost": "npx hardhat run --network localhost scripts/deploy.js",
+    "prepare-demo": "npx hardhat compile && npx hardhat run --network localhost scripts/deploy.js && npx hardhat add_bounty"
   }
 }

eth/prepare.py

@@ -0,0 +1,38 @@
+
+
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.datasets import load_iris # Load Iris Data
+
+iris = load_iris() # Creating pd DataFrames
+
+iris_df = pd.DataFrame(data= iris.data, columns= iris.feature_names)
+target_df = pd.DataFrame(data= iris.target, columns= ['species'])
+
+def converter(specie):
+    if specie == 0:
+        return 'setosa'
+    elif specie == 1:
+        return 'versicolor'
+    else:
+        return 'virginica'
+
+target_df['species'] = target_df['species'].apply(converter)# Concatenate the DataFrames
+iris_df = pd.concat([iris_df, target_df], axis= 1)
+
+# Converting Objects to Numerical dtype
+iris_df.drop('species', axis= 1, inplace= True)
+target_df = pd.DataFrame(columns= ['species'], data= iris.target)
+iris_df = pd.concat([iris_df, target_df], axis= 1)# Variables
+X= iris_df.drop(labels= 'sepal length (cm)', axis= 1)
+y= iris_df['sepal length (cm)']
+
+# Splitting the Dataset 
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size= 0.13, random_state= 101)
+
+X = X_test.values[:]
+Yt_expected = y_test.values[:].reshape(-1, 1)
+
+np.save('dataset/X.npy',X)
+np.save('dataset/Y.npy',Yt_expected)

eth/settings.json

@@ -16,5 +16,5 @@
   "m": 20,
   "p": 4,
   "n": 1,
-  "mse_target": 0.11232375
+  "mse_target": 0.07864
 }