new folder who dis

dwarves/scout.py

@@ -0,0 +1,67 @@
+# Regression/Prediction (Totally gonna do later trust bro)
+from sklearn.linear_model import LinearRegression
+from sklearn.impute import SimpleImputer
+from sklearn import preprocessing
+from scipy.spatial import distance
+import scipy.stats as stats
+import numpy as np
+import pandas as pd
+
+
+def cure_depression(dataset):
+    # this is pog
+    numeric = dataset.select_dtypes(include=np.number)
+    numeric_columns = numeric.columns
+    dataset[numeric_columns] = dataset[numeric_columns].interpolate(
+        method="linear", limit_direct="forward"
+    )
+    # fuck around and find out with other methods maybe idk
+    return dataset
+
+
+# Fuck you and whateevr you fucking stand for you dumb whore faggot
+def regression_expression(dataset, column, missing_value):
+    lr = LinearRegression()
+    numeric = dataset.select_dtypes(include=np.number)
+    # Migrate this to digger
+    # the fookin nulls
+    testdf = numeric[numeric[column].isnull() == False]
+    testdf = testdf[testdf[column] != 0]
+    # the non nulls and non 0s
+    traindf = numeric[numeric[column].isnull() == False]
+    traindf = traindf[traindf[column] != 0]
+    # print(traindf.head(20))
+    # end of migration
+
+    y = traindf[column]
+    traindf.drop(column, axis=1, inplace=True)
+    lr.fit(traindf, y)
+    pred = lr.predict(testdf)
+    # can't put this in data set directly because length no match
+    # join testdf and traindf to form dataset perhaps??
+    testdf[column] = pred
+    print(testdf.head(30))
+
+# https://scikit-learn.org/stable/modules/preprocessing.html#preprocessing
+# That helps ^
+# This boi should work, idk i'm implementing blindly
+def scaling_zscore(dataframe, col):
+    return stats.zscore(dataframe[col],axis = 0,  nan_policy= "omit")
+
+def dissimilarity(row_arr):
+    row_arr = row_arr.select_dtypes(include = np.number)
+    row_arr = row_arr.drop('Rank', axis = 1)
+    
+    print("          |  Entry 1 | Entry 2 | Entry 3 | Entry 4 | Entry 5 |")
+    for i in range(len(row_arr)):
+        print("Entry " , i + 1, " | ", end = "")
+        for j in range(len(row_arr)):
+            eucDist = distance.euclidean(row_arr.iloc[i], row_arr.iloc[j])
+            print(" {:#.6g} |".format(eucDist), end = "")
+        print("\n")
+
+def scaling_range(datashitter, col):
+    nonnull = datashitter[col].isna()
+    minmax_scaler = preprocessing.MinMaxScaler()
+    trainer = minmax_scaler.fit_transform(datashitter[nonnull])
+    return minmax_scaler.transform(datashitter[col])