Merge branch 'master' of https://github.com/titanscout2022/tr2022-strategy

.gitignore

@@ -1,3 +1,8 @@
 
 benchmark_data.csv
 
+
+data analysis/keys/keytemp.json
+data analysis/__pycache__/analysis.cpython-37.pyc
+apps/android/source/app/src/main/res/drawable-v24/uuh.png
+apps/android/source/app/src/main/java/com/example/titanscouting/tits.java

README.md

@@ -0,0 +1,3 @@
+# tr2022-strategy
+Titan Robotics 2022 Strategy Team Repository
+TIT SCOUT

apps/android/source/app/src/main/AndroidManifest.xml

@@ -2,7 +2,7 @@
 <manifest xmlns:android="http://schemas.android.com/apk/res/android"
     package="com.example.titanscouting">
 
-    <uses-permission android:name="android.permission.INTERNET"/>
+    <uses-permission android:name="android.permission.INTERNET" />
 
     <application
         android:allowBackup="true"
@@ -10,20 +10,19 @@
         android:label="@string/app_name"
         android:roundIcon="@drawable/binoculars_big"
         android:supportsRtl="true"
-
+        android:theme="@style/AppTheme"
-        android:usesCleartextTraffic="true"
+        android:usesCleartextTraffic="true">
-
+        <activity android:name=".tits"></activity>
-        android:theme="@style/AppTheme">
+        <activity android:name=".launcher">
-
-
-
-        <activity android:name=".MainActivity">
             <intent-filter>
                 <action android:name="android.intent.action.MAIN" />
 
                 <category android:name="android.intent.category.LAUNCHER" />
             </intent-filter>
         </activity>
+        <activity android:name=".MainActivity">
+
+        </activity>
     </application>
 
 </manifest>

apps/android/source/app/src/main/java/com/example/titanscouting/launcher.java

@@ -0,0 +1,49 @@
+package com.example.titanscouting;
+
+import android.support.v7.app.AppCompatActivity;
+import android.os.Bundle;
+import android.app.Activity;
+import android.content.Intent;
+import android.view.Menu;
+import android.view.View;
+import android.view.View.OnClickListener;
+import android.widget.Button;
+import android.widget.EditText;
+public class launcher extends AppCompatActivity {
+
+    Button button;
+    EditText passField;
+
+    @Override
+    protected void onCreate(Bundle savedInstanceState) {
+        super.onCreate(savedInstanceState);
+        setContentView(R.layout.activity_launcher);
+
+        // Locate the button in activity_main.xml
+        button = (Button) findViewById(R.id.launch_button);
+        final EditText passField = (EditText)findViewById(R.id.editText);
+        // Capture button clicks
+        button.setOnClickListener(new OnClickListener() {
+            public void onClick(View arg0) {
+
+                // Start NewActivity.class
+                if(passField.getText().toString().equals("gimmetits")){
+
+                    Intent myIntent = new Intent(launcher.this,
+                            tits.class);
+                    startActivity(myIntent);
+
+                }
+                else {
+                    Intent myIntent = new Intent(launcher.this,
+                            MainActivity.class);
+                    startActivity(myIntent);
+                }
+            }
+        });
+
+    }
+
+
+
+}

apps/android/source/app/src/main/java/com/example/titanscouting/tits.java

@@ -0,0 +1,30 @@
+package com.example.titanscouting;
+
+import android.content.Intent;
+import android.support.v7.app.AppCompatActivity;
+import android.os.Bundle;
+import android.view.View;
+import android.widget.Button;
+import android.widget.EditText;
+
+public class tits extends AppCompatActivity {
+    Button button;
+    @Override
+    protected void onCreate(Bundle savedInstanceState) {
+        super.onCreate(savedInstanceState);
+        setContentView(R.layout.activity_tits);
+
+        button = (Button) findViewById(R.id.button);
+        // Capture button clicks
+        button.setOnClickListener(new View.OnClickListener() {
+            public void onClick(View arg0) {
+
+
+                    Intent myIntent = new Intent(tits.this,
+                            MainActivity.class);
+                    startActivity(myIntent);
+
+            }
+        });
+    }
+}

apps/android/source/app/src/main/res/layout/activity_launcher.xml

@@ -0,0 +1,42 @@
+<?xml version="1.0" encoding="utf-8"?>
+<android.support.constraint.ConstraintLayout xmlns:android="http://schemas.android.com/apk/res/android"
+    xmlns:app="http://schemas.android.com/apk/res-auto"
+    xmlns:tools="http://schemas.android.com/tools"
+    android:layout_width="match_parent"
+    android:layout_height="match_parent"
+    tools:context=".launcher">
+
+    <Button
+        android:id="@+id/launch_button"
+        android:layout_width="253dp"
+        android:layout_height="56dp"
+        android:layout_marginStart="8dp"
+        android:layout_marginLeft="8dp"
+        android:layout_marginTop="8dp"
+        android:layout_marginEnd="8dp"
+        android:layout_marginRight="8dp"
+        android:layout_marginBottom="8dp"
+        android:text="Launch Titan Scouting"
+        app:layout_constraintBottom_toBottomOf="parent"
+        app:layout_constraintEnd_toEndOf="parent"
+        app:layout_constraintStart_toStartOf="parent"
+        app:layout_constraintTop_toTopOf="parent" />
+
+    <EditText
+        android:id="@+id/editText"
+        android:layout_width="wrap_content"
+        android:layout_height="wrap_content"
+        android:layout_marginStart="8dp"
+        android:layout_marginLeft="8dp"
+        android:layout_marginTop="8dp"
+        android:layout_marginEnd="8dp"
+        android:layout_marginRight="8dp"
+        android:layout_marginBottom="8dp"
+        android:ems="10"
+        android:inputType="textPassword"
+        app:layout_constraintBottom_toBottomOf="parent"
+        app:layout_constraintEnd_toEndOf="parent"
+        app:layout_constraintStart_toStartOf="parent"
+        app:layout_constraintTop_toBottomOf="@+id/launch_button"
+        app:layout_constraintVertical_bias="0.0" />
+</android.support.constraint.ConstraintLayout>

apps/android/source/app/src/main/res/layout/activity_tits.xml

@@ -0,0 +1,36 @@
+<?xml version="1.0" encoding="utf-8"?>
+<android.support.constraint.ConstraintLayout xmlns:android="http://schemas.android.com/apk/res/android"
+    xmlns:app="http://schemas.android.com/apk/res-auto"
+    xmlns:tools="http://schemas.android.com/tools"
+    android:layout_width="match_parent"
+    android:layout_height="match_parent"
+    tools:context=".tits">
+
+    <ImageView
+        android:id="@+id/imageView"
+        android:layout_width="372dp"
+        android:layout_height="487dp"
+        android:layout_marginTop="4dp"
+        android:layout_marginBottom="215dp"
+        app:layout_constraintBottom_toBottomOf="parent"
+        app:layout_constraintEnd_toEndOf="parent"
+        app:layout_constraintStart_toStartOf="parent"
+        app:layout_constraintTop_toTopOf="parent"
+        app:srcCompat="@drawable/uuh" />
+
+    <Button
+        android:id="@+id/button"
+        android:layout_width="198dp"
+        android:layout_height="86dp"
+        android:layout_marginStart="8dp"
+        android:layout_marginLeft="8dp"
+        android:layout_marginTop="8dp"
+        android:layout_marginEnd="8dp"
+        android:layout_marginRight="8dp"
+        android:layout_marginBottom="8dp"
+        android:text="Fuck Get Me Out"
+        app:layout_constraintBottom_toBottomOf="parent"
+        app:layout_constraintEnd_toEndOf="parent"
+        app:layout_constraintStart_toStartOf="parent"
+        app:layout_constraintTop_toBottomOf="@+id/imageView" />
+</android.support.constraint.ConstraintLayout>

data analysis/analysis.py

@@ -411,6 +411,7 @@ class objectives:
 def load_csv(filepath):
     with open(filepath, newline = '') as csvfile:
         file_array = list(csv.reader(csvfile))
+        csvfile.close()
     return file_array
 
 def basic_stats(data, method, arg): # data=array, mode = ['1d':1d_basic_stats, 'column':c_basic_stats, 'row':r_basic_stats], arg for mode 1 or mode 2 for column or row
@@ -422,7 +423,7 @@ def basic_stats(data, method, arg): # data=array, mode = ['1d':1d_basic_stats, '
 
         data_t = []
 
-        for i in range (0, len(data) - 1, 1):
+        for i in range (0, len(data), 1):
             data_t.append(float(data[i]))
     
         _mean = mean(data_t)
@@ -558,12 +559,15 @@ def stdev_z_split(mean, stdev, delta, low_bound, high_bound): #returns n-th perc
 def histo_analysis(hist_data, delta, low_bound, high_bound):
 
     if hist_data == 'debug':
-        return ('returns list of predicted values based on historical data; input delta for delta step in z-score and lower and higher bounds in number for standard deviations')
+        return ('returns list of predicted values based on historical data; input delta for delta step in z-score and lower and higher bounds in number of standard deviations')
 
     derivative = []
 
-    for i in range(0, len(hist_data) - 1, 1):
+    for i in range(0, len(hist_data), 1):
-        derivative.append(float(hist_data[i + 1]) - float(hist_data [i]))
+        try:
+            derivative.append(float(hist_data[i - 1]) - float(hist_data [i]))
+        except:
+            pass
 
     derivative_sorted = sorted(derivative, key=int)
     mean_derivative = basic_stats(derivative_sorted,"1d", 0)[0]
@@ -700,7 +704,7 @@ def tanh_regression(x, y):
     
 def r_squared(predictions, targets): # assumes equal size inputs
 
-    return metrics.r2_score(targets, predictions)
+    return metrics.r2_score(np.array(targets), np.array(predictions))
 
 def rms(predictions, targets): # assumes equal size inputs
 
@@ -727,7 +731,7 @@ def calc_overfit(equation, rms_train, r2_train, x_test, y_test):
     r2_test = r_squared(vals, y_test)
     rms_test = rms(vals, y_test)
 
-    return rms_train - rms_test, r2_train - r2_test
+    return r2_train - r2_test
 
 def strip_data(data, mode):
 
@@ -746,16 +750,19 @@ def optimize_regression(x, y, _range, resolution):#_range in poly regression is
         raise error("resolution must be int")
 
     x_train = x
-    y_train = y
+    y_train = []
+
+    for i in range(len(y)):
+        y_train.append(float(y[i]))
 
     x_test = []
     y_test = []
 
-    for i in range (0, math.floor(len(x) * 0.4), 1):
+    for i in range (0, math.floor(len(x) * 0.5), 1):
         index = random.randint(0, len(x) - 1)
 
         x_test.append(x[index])
-        y_test.append(y[index])
+        y_test.append(float(y[index]))
 
         x_train.pop(index)
         y_train.pop(index)
@@ -768,10 +775,13 @@ def optimize_regression(x, y, _range, resolution):#_range in poly regression is
     r2s = []
 
     for i in range (0, _range + 1, 1):
+        try:
             x, y, z = poly_regression(x_train, y_train, i)
             eqs.append(x)
             rmss.append(y)
             r2s.append(z)
+        except:
+            pass
 
     for i in range (1, 100 * resolution + 1):
         try:
@@ -791,11 +801,14 @@ def optimize_regression(x, y, _range, resolution):#_range in poly regression is
         except:
             pass
 
+    try:
         x, y, z = tanh_regression(x_train, y_train)
 
         eqs.append(x)
         rmss.append(y)
         r2s.append(z)
+    except:
+        pass
     
     for i in range (0, len(eqs), 1): #marks all equations where r2 = 1 as they 95% of the time overfit the data
         if r2s[i] == 1:
@@ -814,6 +827,7 @@ def optimize_regression(x, y, _range, resolution):#_range in poly regression is
     overfit = []
 
     for i in range (0, len(eqs), 1):
+
         overfit.append(calc_overfit(eqs[i], rmss[i], r2s[i], x_test, y_test))
             
     return eqs, rmss, r2s, overfit
@@ -829,17 +843,16 @@ def select_best_regression(eqs, rmss, r2s, overfit, selector):
 
     if selector == "min_overfit":
 
-		ind = np.argmax(overfit)
+        ind = np.argmin(overfit)
 
         b_eq = eqs[ind]
         b_rms = rmss[ind]
         b_r2 = r2s[ind]
         b_overfit = overfit[ind]
 
-	if selector == "max_rmss":
+    if selector == "max_r2s":
-
-		ind = np.argmax(rmss)
 
+        ind = np.argmax(r2s)
         b_eq = eqs[ind]
         b_rms = rmss[ind]
         b_r2 = r2s[ind]

data analysis/data/aSetOfData.csv

@@ -0,0 +1,5 @@
+2022,4,5,10,15,8,954978,84,74
+2011,3,6,9,12,15,7,856,9
+1101,4,16,32,64,128,2,4234,-56
+821374,7,8,9,79.33333333,170.3333333,636647,5874.666667,-121
+5,4,8,8,103.8333333,230.3333333,1114135,7949.666667,-186

data analysis/data/bdata.csv

@@ -0,0 +1,5 @@
+3.1630010613819266,9.4292307303114,-0.323251889179911,-5.386181559067866,2.534879621508633,-5.165597428986722,6.781430809047265,-0.6282127632735541,-1.0861042779691985,9.35487366197819,8.797857763983743,-1.7228656825441355,-8.537192856364065,5.052775825388903,-5.801718614169187,-3.991567829343965,-6.354838195228005,-5.789567658308035,-2.234361028326452,-7.824326717389576,9.944153435265441,-2.6250058262846743,5.187785540324899,-7.817733885932993,-0.5495374257657222,-1.9071654677837895,5.573891899068942,-1.488902249404024,0.1466900371151887,-1.9859573811436793,-7.2484840322743,1.1313354023315867,-7.5618652049793855,-5.827244176508111,3.545169414494671,6.308215883868659,-6.718473082814393,8.377856694673874,-8.16137266764721,-5.075101123286288,-4.5852911742407,6.719021169503613,-6.029113281204543,5.273328597459566,0.0763770865509823,9.28885677894446,-7.947793863113885,7.845554524426525,-8.521879358485565,0.9347947446861724,-0.28152137414332934,9.527815722615298,-7.781986361995141,2.51994233096182,-8.375584210551812,7.579152243875466,-6.011614079234309,7.9368523400199,5.794364333401118,0.3270958957976884,3.343149418463785,1.187118681195516,5.683636971125912,3.058057837202666,2.9731154581416703,-5.951054389944959,-3.938143670001601,-8.423951328854164,2.0094359968578157,5.976845213102051,-4.498822138796992,5.810899623653306,-2.827881055139718,-1.8019945971538682,-4.9529807256756175,-1.9837251605735169,6.296210829315669,-3.2065733117364754,4.453313976934407,2.5735548193312408,5.146019114708203,-8.390100760859474,-5.111322602030599,4.295829898355002,2.883101334770684,6.65043004394818,-8.552982078286695,6.058368688959515,-9.298443633133909,5.416227761836057,3.1281090859797303,-2.7702280295615633,-5.368155844397277,2.684599018010413,6.603278656476263,0.493963745183553,9.93340040961121,0.8827110366761914,-0.44455956717372125,-6.748670065028741
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data analysis/data/scores.csv

@@ -0,0 +1 @@
+2022, 21, 23, 39, 50, 89, 97, 191, 213, 233, 236, 272, 289, 308, 310, 314, 317, 329, 355, 428, 436
2011, 1, 25, 34, 106, 112, 132, 146, 167, 184, 223, 256, 267, 304, 337, 339, 370, 411, 460, 485, 494
1101, 7, 11, 55, 81, 92, 144, 151, 157, 165, 174, 177, 220, 241, 312, 320, 341, 343, 347, 419, 473
821374, 6, 9, 11, 28, 31, 194, 200, 201, 246, 298, 340, 351, 355, 364, 388, 395, 414, 422, 429, 442
5, 33, 73, 102, 103, 137, 183, 200, 212, 218, 242, 249, 264, 294, 302, 310, 378, 400, 408, 457, 473

data analysis/data/teams.csv

@@ -0,0 +1 @@
+2022
2011
1101
821374
5

data analysis/keys/firebasekey.json

@@ -0,0 +1,12 @@
+{
+  "type": "service_account",
+  "project_id": "titanscoutandroid",
+  "private_key_id": "e7cde706a13a6fade555cce2bc46ee53f05a0b11",
+  "private_key": "-----BEGIN PRIVATE KEY-----\(gottem)/B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\nvJCSRvjRtxyFafuX4YQzWu0=\n-----END PRIVATE KEY-----\n",
+  "client_email": "firebase-adminsdk-wpsvx@titanscoutandroid.iam.gserviceaccount.com",
+  "client_id": "114864465329268712237",
+  "auth_uri": "https://accounts.google.com/o/oauth2/auth",
+  "token_uri": "https://oauth2.googleapis.com/token",
+  "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
+  "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/firebase-adminsdk-wpsvx%40titanscoutandroid.iam.gserviceaccount.com"
+}

data analysis/superscript.py

@@ -1,12 +1,30 @@
-
+#Titan Robotics Team 2022: Super Script
-#Titan Robotics Team 2022: Data Analysis Script
 #Written by Arthur Lu & Jacob Levine
 #Notes:
 #setup:
 
-__version__ = "1.0.1.000"
+__version__ = "1.0.5.002"
 
 __changelog__ = """changelog:
+1.0.5.002:
+    - more information given
+    - performance improvements
+1.0.5.001:
+    - grammar
+1.0.5.000:
+    - service now iterates forever
+    - ready for production other than pulling json data
+1.0.4.001:
+    - grammar fixes
+1.0.4.000:
+    - actually pushes to firebase
+1.0.3.001:
+    - processes data more efficiently
+1.0.3.000:
+    - actually processes data
+1.0.2.000:
+    - added data reading from folder
+    - nearly crashed computer reading from 20 GiB of data
 1.0.1.000:
     - added data reading from file
     - added superstructure to code
@@ -19,8 +37,206 @@ __author__ = (
     "Jacob Levine <jlevine@ttic.edu>,"
     )
 
+import firebase_admin
+from firebase_admin import credentials
+from firebase_admin import firestore
 import analysis
 import titanlearn
 import visualization
+import os
+import sys
+import warnings
+import glob
+import numpy as np
+import time
 
-data = analysis.load_csv("data/data.csv")
+def titanservice():
+    
+    print("[OK] loading data")
+
+    start = time.time()
+    
+    source_dir = 'data'
+    file_list = glob.glob(source_dir + '/*.csv') #supposedly sorts by alphabetical order, skips reading teams.csv because of redundancy
+    data = []
+    files = [fn for fn in glob.glob('data/*.csv') 
+             if not (os.path.basename(fn).startswith('teams'))] #scores will be handled sperately
+
+    for i in files:
+            data.append(analysis.load_csv(i))
+
+    stats = []
+    measure_stats = []
+    teams = analysis.load_csv("data/teams.csv")
+    scores = analysis.load_csv("data/scores.csv")
+
+    end = time.time()
+
+    print("[OK] loaded data in " + str(end - start) + "seconds")
+
+    #assumes that team number is in the first column, and that the order of teams is the same across all files
+    #unhelpful comment
+    for measure in data: #unpacks 3d array into 2ds
+
+        measure_stats = []
+
+        for i in range(len(measure)): #unpacks into specific teams
+
+                    ofbest_curve = [None]
+                    r2best_curve = [None]
+
+                    line = measure[i]
+
+                    #print(line)
+
+                    x = list(range(len(line)))
+                    eqs, rmss, r2s, overfit = analysis.optimize_regression(x, line, 10, 1)
+
+                    beqs, brmss, br2s, boverfit = analysis.select_best_regression(eqs, rmss, r2s, overfit, "min_overfit")
+
+                    #print(eqs, rmss, r2s, overfit)
+                    
+                    ofbest_curve.append(beqs)
+                    ofbest_curve.append(brmss)
+                    ofbest_curve.append(br2s)
+                    ofbest_curve.append(boverfit)
+                    ofbest_curve.pop(0)
+
+                    #print(ofbest_curve)
+
+                    beqs, brmss, br2s, boverfit = analysis.select_best_regression(eqs, rmss, r2s, overfit, "max_r2s")
+
+                    r2best_curve.append(beqs)
+                    r2best_curve.append(brmss)
+                    r2best_curve.append(br2s)
+                    r2best_curve.append(boverfit)
+                    r2best_curve.pop(0)
+
+                    #print(r2best_curve)
+
+                    
+                    measure_stats.append(teams[i] + list(analysis.basic_stats(line, 0, 0)) + list(analysis.histo_analysis(line, 1, -3, 3)) + ofbest_curve + r2best_curve)
+
+            stats.append(list(measure_stats))
+            nishant = []
+            
+            for i in range(len(scores)):
+
+                    ofbest_curve = [None]
+                    r2best_curve = [None]
+
+                    line = measure[i]
+
+                    #print(line)
+
+                    x = list(range(len(line)))
+                    eqs, rmss, r2s, overfit = analysis.optimize_regression(x, line, 10, 1)
+
+                    beqs, brmss, br2s, boverfit = analysis.select_best_regression(eqs, rmss, r2s, overfit, "min_overfit")
+
+                    #print(eqs, rmss, r2s, overfit)
+                    
+                    ofbest_curve.append(beqs)
+                    ofbest_curve.append(brmss)
+                    ofbest_curve.append(br2s)
+                    ofbest_curve.append(boverfit)
+                    ofbest_curve.pop(0)
+
+                    #print(ofbest_curve)
+
+                    beqs, brmss, br2s, boverfit = analysis.select_best_regression(eqs, rmss, r2s, overfit, "max_r2s")
+
+                    r2best_curve.append(beqs)
+                    r2best_curve.append(brmss)
+                    r2best_curve.append(br2s)
+                    r2best_curve.append(boverfit)
+                    r2best_curve.pop(0)
+
+                    #print(r2best_curve)
+                    
+                    z = len(scores[0]) + 1
+                    nis_num = []
+
+                    nis_num.append(eval(str(ofbest_curve[0])))
+                    nis_num.append(eval(str(r2best_curve[0])))
+
+                    nis_num.append((eval(ofbest_curve[0]) + eval(r2best_curve[0])) / 2)
+
+                    nishant.append(teams[i] + nis_num)
+                
+    json_out = {}
+    score_out = {}
+
+    #print(stats)
+                    
+    for i in range(len(teams)):
+            json_out[str(teams[i][0])] = (stats[0][i])
+
+    for i in range(len(teams)):
+            score_out[str(teams[i][0])] = (nishant[i])
+
+    print(json_out)
+
+    #print(json_out.get('5'))
+
+    location = db.collection(u'stats').document(u'stats-noNN')
+    for i in range(len(teams)):
+        general_general_stats = location.collection(teams[i][0])
+        for j in range(len(files)):
+            general_general_stats.document(files[j]).set({'stats':json_out.get(teams[i][0])})
+
+    for i in range(len(teams)):
+        nnum = location.collection(teams[i][0]).document(u'nishant_number').set({'nishant':score_out.get(teams[i][0])})
+
+    #general_general_stats.collection().document('stats').set()
+    #db.collection(u'stats').document(u'stats-noNN').set(score_out)
+
+def pulldata():
+    #TODO
+    pass
+
+def service():
+
+    while True:
+
+        pulldata()
+
+        start = time.time()
+
+        print("[OK] starting calculations")
+
+        fucked = False
+        
+        for i in range(0, 5):
+            try:
+                titanservice()
+                break
+            except:
+                if (i != 4):
+                    print("[WARNING] failed, trying " + str(5 - i - 1) + " more times")
+                else:
+                    print("[ERROR] failed to compute data, skipping")
+                    fucked = True
+
+        end = time.time()
+        if (fucked == True):
+
+            break
+
+        else:
+            
+            print("[OK] finished calculations")
+
+        print("[OK] waiting: " + str(300 - (end - start)) + " seconds" + "\n")
+
+        time.sleep(300 - (end - start)) #executes once every 5 minutes
+
+warnings.simplefilter("ignore")
+# Use a service account
+cred = credentials.Certificate('keys/keytemp.json')
+firebase_admin.initialize_app(cred)
+
+db = firestore.client()
+
+service() #finally we write something that isn't a function definition
+#titanservice()

data analysis/titanlearn.py

@@ -26,7 +26,7 @@ __all__ = [
 import torch
 import warnings
 from collections import OrderedDict
-from sklearn import metrics
+from sklearn import metrics, datasets
 import numpy as np
 import matplotlib.pyplot as plt
 import math
@@ -35,24 +35,24 @@ import math
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
 #linear_nn: creates a fully connected network given params
-def linear_nn(in_dim, hidden_dim, out_dim, num_hidden, act_fn="tanh", end="softmax"):
+def linear_nn(in_dim, hidden_dim, out_dim, num_hidden, act_fn="tanh", end="none"):
     if act_fn.lower()=="tanh":
-        k=OrderedDict([("in", torch.nn.Linear(in_dim,hidden_dim)), ('tanh0', torch.nn.Tanh())])
+        k=OrderedDict([("in", torch.nn.Linear(in_dim,hidden_dim))])
         for i in range(num_hidden):
             k.update({"lin"+str(i+1): torch.nn.Linear(hidden_dim,hidden_dim), "tanh"+str(i+1):torch.nn.Tanh()})
 
     elif act_fn.lower()=="sigmoid":
-        k=OrderedDict([("in", torch.nn.Linear(in_dim,hidden_dim)), ('sig0', torch.nn.Sigmoid())])
+        k=OrderedDict([("in", torch.nn.Linear(in_dim,hidden_dim))])
         for i in range(num_hidden):
             k.update({"lin"+str(i+1): torch.nn.Linear(hidden_dim,hidden_dim), "sig"+str(i+1):torch.nn.Sigmoid()})
 
     elif act_fn.lower()=="relu":
-        k=OrderedDict([("in", torch.nn.Linear(in_dim,hidden_dim)), ('relu0', torch.nn.ReLU())])
+        k=OrderedDict([("in", torch.nn.Linear(in_dim,hidden_dim))])
         for i in range(num_hidden):
             k.update({"lin"+str(i+1): torch.nn.Linear(hidden_dim,hidden_dim), "relu"+str(i+1):torch.nn.ReLU()})
 
     elif act_fn.lower()=="leaky relu":
-        k=OrderedDict([("in", torch.nn.Linear(in_dim,hidden_dim)), ('lre0', torch.nn.LeakyReLU())])
+        k=OrderedDict([("in", torch.nn.Linear(in_dim,hidden_dim))])
         for i in range(num_hidden):
             k.update({"lin"+str(i+1): torch.nn.Linear(hidden_dim,hidden_dim), "lre"+str(i+1):torch.nn.LeakyReLU()})
     else:
@@ -72,7 +72,7 @@ def linear_nn(in_dim, hidden_dim, out_dim, num_hidden, act_fn="tanh", end="softm
     return torch.nn.Sequential(k)
 
 #train_sgd_simple: trains network using SGD
-def train_sgd_simple(net, data, ground, dev=None, devg=None, iters=1000, learnrate=1e-4, testevery=1, graphsaveloc=None, modelsaveloc=None, loss="mse"):
+def train_sgd_simple(net, evalType, data, ground, dev=None, devg=None, iters=1000, learnrate=1e-4, testevery=1, graphsaveloc=None, modelsaveloc=None, loss="mse"):
     model=net.to(device)
     data=data.to(device)
     ground=ground.to(device)
@@ -81,7 +81,7 @@ def train_sgd_simple(net, data, ground, dev=None, devg=None, iters=1000, learnra
     losses=[]
     dev_losses=[]
     if loss.lower()=="mse":
-        loss_fn = torch.nn.MSELoss(reduction='sum')
+        loss_fn = torch.nn.MSELoss()
     elif loss.lower()=="cross entropy":
         loss_fn = torch.nn.CrossEntropyLoss()
     elif loss.lower()=="nll":
@@ -96,21 +96,34 @@ def train_sgd_simple(net, data, ground, dev=None, devg=None, iters=1000, learnra
         if i%testevery==0:
             with torch.no_grad():
                 output = model(data)
-                ap = metrics.average_precision_score(ground.numpy(), output.numpy())
+                if evalType == "ap":
+                    ap = metrics.average_precision_score(ground.cpu().numpy(), output.cpu().numpy())
+                if evalType == "regression":
+                    ap = metrics.explained_variance_score(ground.cpu().numpy(), output.cpu().numpy())
                 losses.append(ap)
                 print(str(i)+": "+str(ap))
                 plt.plot(np.array(range(0,i+1,testevery)),np.array(losses), label="train AP")
                 if dev != None:
                     output = model(dev)
+                    print(evalType)
+                    if evalType == "ap":
+
                         ap = metrics.average_precision_score(devg.numpy(), output.numpy())
                         dev_losses.append(ap)
                         plt.plot(np.array(range(0,i+1,testevery)),np.array(losses), label="dev AP")
+                    elif evalType == "regression":
+                        ev = metrics.explained_variance_score(devg.numpy(), output.numpy())
+                        dev_losses.append(ev)
+                        plt.plot(np.array(range(0,i+1,testevery)),np.array(losses), label="dev EV")
+
+
                 if graphsaveloc != None:
                     plt.savefig(graphsaveloc+".pdf")
         with torch.enable_grad():
             optimizer.zero_grad()
             output = model(data)
             loss = loss_fn(output, ground)
+            print(loss.item())
             loss.backward()
             optimizer.step()
     if modelsaveloc != None:
@@ -128,7 +141,7 @@ def train_sgd_minibatch(net, data, ground, dev=None, devg=None, epoch=100, batch
     losses=[]
     dev_losses=[]
     if loss.lower()=="mse":
-        loss_fn = torch.nn.MSELoss(reduction='sum')
+        loss_fn = torch.nn.MSELoss()
     elif loss.lower()=="cross entropy":
         loss_fn = torch.nn.CrossEntropyLoss()
     elif loss.lower()=="nll":
@@ -138,7 +151,7 @@ def train_sgd_minibatch(net, data, ground, dev=None, devg=None, epoch=100, batch
     else:
         warnings.warn("Did not specify a valid loss function. Returning nothing.")
         return None
-    optimizer=torch.optim.SGD(model.parameters(), lr=learnrate)
+    optimizer=torch.optim.LBFGS(model.parameters(), lr=learnrate)
     itercount=0
     for i in range(epoch):
         print("EPOCH "+str(i)+" OF "+str(epoch-1))
@@ -176,3 +189,13 @@ def train_sgd_minibatch(net, data, ground, dev=None, devg=None, epoch=100, batch
         torch.save(model, modelsaveloc)
     plt.show()
     return model
+
+def retyuoipufdyu():
+
+    data = torch.tensor(datasets.fetch_california_housing()['data']).to(torch.float)
+    ground = datasets.fetch_california_housing()['target']
+    ground=torch.tensor(ground).to(torch.float)
+    model = linear_nn(8, 100, 1, 20, act_fn = "relu")
+    print(model)
+    return train_sgd_simple(model,"regression", data, ground, learnrate=1e-4, iters=1000)
+#retyuoipufdyu()