diff --git a/.gitignore b/.gitignore
index 8a6040ad..f52c5638 100644
--- a/.gitignore
+++ b/.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
diff --git a/README.md b/README.md
new file mode 100644
index 00000000..e7e10dbb
--- /dev/null
+++ b/README.md
@@ -0,0 +1,3 @@
+# tr2022-strategy
+Titan Robotics 2022 Strategy Team Repository
+TIT SCOUT
diff --git a/apps/android/apk/1.0.0.002/app-debug.apk b/apps/android/apk/1.0.0.002/app-debug.apk
new file mode 100644
index 00000000..07204948
Binary files /dev/null and b/apps/android/apk/1.0.0.002/app-debug.apk differ
diff --git a/apps/android/apk/1.0.0.003/app-debug.apk b/apps/android/apk/1.0.0.003/app-debug.apk
new file mode 100644
index 00000000..c5ca98f8
Binary files /dev/null and b/apps/android/apk/1.0.0.003/app-debug.apk differ
diff --git a/apps/android/source/app/src/main/AndroidManifest.xml b/apps/android/source/app/src/main/AndroidManifest.xml
index ea1d3d50..abe95ceb 100644
--- a/apps/android/source/app/src/main/AndroidManifest.xml
+++ b/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:usesCleartextTraffic="true"
-
-        android:theme="@style/AppTheme">
-
-
-
-        <activity android:name=".MainActivity">
+        android:theme="@style/AppTheme"
+        android:usesCleartextTraffic="true">
+        <activity android:name=".tits"></activity>
+        <activity android:name=".launcher">
             <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>
\ No newline at end of file
diff --git a/apps/android/source/app/src/main/java/com/example/titanscouting/launcher.java b/apps/android/source/app/src/main/java/com/example/titanscouting/launcher.java
new file mode 100644
index 00000000..93ad4434
--- /dev/null
+++ b/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);
+                }
+            }
+        });
+
+    }
+
+
+
+}
diff --git a/apps/android/source/app/src/main/java/com/example/titanscouting/tits.java b/apps/android/source/app/src/main/java/com/example/titanscouting/tits.java
new file mode 100644
index 00000000..336684b3
--- /dev/null
+++ b/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);
+
+            }
+        });
+    }
+}
diff --git a/apps/android/source/app/src/main/res/layout/activity_launcher.xml b/apps/android/source/app/src/main/res/layout/activity_launcher.xml
new file mode 100644
index 00000000..c1b4f2c5
--- /dev/null
+++ b/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>
\ No newline at end of file
diff --git a/apps/android/source/app/src/main/res/layout/activity_tits.xml b/apps/android/source/app/src/main/res/layout/activity_tits.xml
new file mode 100644
index 00000000..050ae585
--- /dev/null
+++ b/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>
\ No newline at end of file
diff --git a/data analysis/__pycache__/analysis.cpython-37.pyc b/data analysis/__pycache__/analysis.cpython-37.pyc
index b9e6f345..5d9f4727 100644
Binary files a/data analysis/__pycache__/analysis.cpython-37.pyc and b/data analysis/__pycache__/analysis.cpython-37.pyc differ
diff --git a/data analysis/__pycache__/superscript.cpython-37.pyc b/data analysis/__pycache__/superscript.cpython-37.pyc
new file mode 100644
index 00000000..8cb176ff
Binary files /dev/null and b/data analysis/__pycache__/superscript.cpython-37.pyc differ
diff --git a/data analysis/__pycache__/titanlearn.cpython-37.pyc b/data analysis/__pycache__/titanlearn.cpython-37.pyc
index 4bc942f5..5877577c 100644
Binary files a/data analysis/__pycache__/titanlearn.cpython-37.pyc and b/data analysis/__pycache__/titanlearn.cpython-37.pyc differ
diff --git a/data analysis/__pycache__/visualization.cpython-37.pyc b/data analysis/__pycache__/visualization.cpython-37.pyc
index cdbce53b..14d8a833 100644
Binary files a/data analysis/__pycache__/visualization.cpython-37.pyc and b/data analysis/__pycache__/visualization.cpython-37.pyc differ
diff --git a/data analysis/analysis.py b/data analysis/analysis.py
index 6fc315a3..479f79de 100644
--- a/data analysis/analysis.py	
+++ b/data analysis/analysis.py	
@@ -16,34 +16,34 @@ __changelog__ = """changelog:
 1.0.8.004:
     - removed a few unused dependencies
 1.0.8.003:
-	- added p_value function
+    - added p_value function
 1.0.8.002:
-	- updated __all__ correctly to contain changes made in v 1.0.8.000 and v 1.0.8.001
+    - updated __all__ correctly to contain changes made in v 1.0.8.000 and v 1.0.8.001
 1.0.8.001:
-	- refactors
-	- bugfixes
+    - refactors
+    - bugfixes
 1.0.8.000:
-	- depreciated histo_analysis_old
-	- depreciated debug
-	- altered basic_analysis to take array data instead of filepath
-	- refactor
-	- optimization
+    - depreciated histo_analysis_old
+    - depreciated debug
+    - altered basic_analysis to take array data instead of filepath
+    - refactor
+    - optimization
 1.0.7.002:
-	- bug fixes
+    - bug fixes
 1.0.7.001:
-	- bug fixes
+    - bug fixes
 1.0.7.000:
-	- added tanh_regression (logistical regression)
-	- bug fixes
+    - added tanh_regression (logistical regression)
+    - bug fixes
 1.0.6.005:
-	- added z_normalize function to normalize dataset
-	- bug fixes
+    - added z_normalize function to normalize dataset
+    - bug fixes
 1.0.6.004:
-	- bug fixes
+    - bug fixes
 1.0.6.003:
-	- bug fixes
+    - bug fixes
 1.0.6.002:
-	- bug fixes
+    - bug fixes
 1.0.6.001:
     - corrected __all__ to contain all of the functions
 1.0.6.000:
@@ -152,7 +152,7 @@ from sklearn import *
 #import statistics <-- statistics.py functions have been integrated into analysis.py as of v 1.0.3.002
 import time
 import torch
-
+    
 class error(ValueError):
     pass
 
@@ -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):
-        derivative.append(float(hist_data[i + 1]) - float(hist_data [i]))
+    for i in range(0, len(hist_data), 1):
+        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
 
@@ -723,11 +727,11 @@ def calc_overfit(equation, rms_train, r2_train, x_test, y_test):
         z = x_test[i]
 
         exec("vals.append(" + equation + ")")
-        
+
     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):
-    	x, y, z = poly_regression(x_train, y_train, i)
-    	eqs.append(x)
-    	rmss.append(y)
-    	r2s.append(z)
+        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
 
-    x, y, z = tanh_regression(x_train, y_train)
+    try:
+        x, y, z = tanh_regression(x_train, y_train)
 
-    eqs.append(x)
-    rmss.append(y)
-    r2s.append(z)
+        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:
@@ -814,38 +827,38 @@ 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
 
 def select_best_regression(eqs, rmss, r2s, overfit, selector):
 
-	b_eq = ""
-	b_rms = 0
-	b_r2 = 0
-	b_overfit = 0
+    b_eq = ""
+    b_rms = 0
+    b_r2 = 0
+    b_overfit = 0
 
-	ind = 0
+    ind = 0
 
-	if selector == "min_overfit":
+    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]
+        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]
+        b_overfit = overfit[ind]
 
-		b_eq = eqs[ind]
-		b_rms = rmss[ind]
-		b_r2 = r2s[ind]
-		b_overfit = overfit[ind]
-
-	return b_eq, b_rms, b_r2, b_overfit
+    return b_eq, b_rms, b_r2, b_overfit
 
 def p_value(x, y): #takes 2 1d arrays
 	
@@ -877,7 +890,7 @@ def basic_analysis(data): #assumes that rows are the independent variable and co
 
 def benchmark(x, y):
 
-    start_g = time.time()
+    start_g = time.time() 
     generate_data("data/data.csv", x, y, -10, 10)
     end_g = time.time()
 
diff --git a/data analysis/data/aSetOfData.csv b/data analysis/data/aSetOfData.csv
new file mode 100644
index 00000000..643d2d23
--- /dev/null
+++ b/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
diff --git a/data analysis/data/bdata.csv b/data analysis/data/bdata.csv
new file mode 100644
index 00000000..a2160ba8
--- /dev/null
+++ b/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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diff --git a/data analysis/data/data.csv b/data analysis/data/data.csv
index 2fff51ba..14b7ad59 100644
--- a/data analysis/data/data.csv	
+++ b/data analysis/data/data.csv	
@@ -1,100 +1,5 @@
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--2.1342958590327044,2.3676493594110255,3.2351604081317458,4.050136213716883,-0.3917609094130192,0.2592746436527431,6.396662963467882,-0.9575222883996872,5.44075456196958,-4.303118088139417,-4.059633951861732,-0.059554306423354575,1.7431002678445964,-8.484290577217596,2.8219151882004887,1.464813047935948,0.14718927940822013,-0.015001113713937642,4.152058830911127,-5.263410299752131,-9.183341010380968,-7.022287055206142,-3.4172501435917884,7.427376688475842,-6.184526136949684,-6.6942388973656675,-5.714318874517195,-0.008389160553221586,9.24262487973419,0.5219330841068892,0.9730118169596693,-3.8524379978185337,-4.145530465100482,2.433444620695454,0.8224805333487772,-8.269702163901462,-9.489889375195656,0.4399896224600752,-1.491937686725258,8.059008141267924,-6.242982250488276,3.3584883072962857,5.5662562084657665,6.609733586625289,0.8820801304724988,0.9130453910870369,7.210864545375813,-8.333448342638544,-6.401137599232647,-2.083684679944053,-9.321289608882072,4.2213782337401256,-0.8421591655432294,0.22134131646782151,9.076023502237366,-5.4143350248939,8.675734541338386,7.956664452967704,-3.075919525438109,9.867907237925941,-0.7695293853003999,-6.340177982495567,-2.403744339820701,4.243685582946949,7.208857961402792,9.20648037365089,9.431863338841275,4.544975191406149,8.546964629782398,2.557316973456878,-5.065851511109325,-1.7897132220284497,-0.80799609535263,-3.7936290508662545,8.24744800087527,7.7780967933890075,6.013689348282536,4.474975822474388,-4.0926837904865305,2.4222936189369992,4.164671196793979,9.034838789889662,8.646020668767346,-2.983774127010421,3.0641392810791164,-8.640996407690206,-5.378676245167984,4.627631442474367,1.5470415538619875,6.796226129577054,-8.74466927442172,0.22028404032345605,-3.8580014405262553,-5.291748711955693,9.383633752843203,3.4002438960583703,-2.3499528021715665,3.972764111678533,-3.5632989508211406,8.924620910325022
+2022,1,2,3,4,5,6,7,8
+2011,5,6,7,8,9,10,11,12
+1101,1000,2000,3000,4000,5000,6000,7000,8000
+821374,0,0,0,0,0,0,0,1
+5,3,5,6,7,8.5,9.8,11.1,12.4
diff --git a/data analysis/data/scores.csv b/data analysis/data/scores.csv
new file mode 100644
index 00000000..5562ec8a
--- /dev/null
+++ b/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
\ No newline at end of file
diff --git a/data analysis/data/teams.csv b/data analysis/data/teams.csv
new file mode 100644
index 00000000..a49e3650
--- /dev/null
+++ b/data analysis/data/teams.csv	
@@ -0,0 +1 @@
+2022
2011
1101
821374
5
\ No newline at end of file
diff --git a/data analysis/keys/firebasekey.json b/data analysis/keys/firebasekey.json
new file mode 100644
index 00000000..b7a073ed
--- /dev/null
+++ b/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"
+}
\ No newline at end of file
diff --git a/data analysis/superscript.py b/data analysis/superscript.py
index 2e7c1fd1..68a99a36 100644
--- a/data analysis/superscript.py	
+++ b/data analysis/superscript.py	
@@ -1,17 +1,35 @@
-
-#Titan Robotics Team 2022: Data Analysis Script
+#Titan Robotics Team 2022: Super 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
+    - added data reading from file
+    - added superstructure to code
 1.0.0.000:
-	- added import statements (revolutionary)
+    - added import statements (revolutionary)
 """ 
 
 __author__ = (
@@ -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")
\ No newline at end of file
+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()
diff --git a/data analysis/titanlearn.py b/data analysis/titanlearn.py
index 9fab3a11..a32bceaa 100644
--- a/data analysis/titanlearn.py	
+++ b/data analysis/titanlearn.py	
@@ -1,178 +1,201 @@
-#Titan Robotics Team 2022: ML Module
-#Written by Arthur Lu & Jacob Levine
-#Notes:
-#   this should be imported as a python module using 'import titanlearn'
-#   this should be included in the local directory or environment variable
-#   this module has not been optimized for multhreaded computing
-#   this module learns from its mistakes far faster than 2022's captains
-#setup:
-
-__version__ = "1.0.0.001"
-
-#changelog should be viewed using print(analysis.__changelog__)
-__changelog__ = """changelog:
-1.0.0.xxx:
-    -added generation of ANNS, basic SGD training"""
-__author__ = (
-    "Arthur Lu <arthurlu@ttic.edu>, "
-    "Jacob Levine <jlevine@ttic.edu>,"
-    )
-__all__ = [
-    'linear_nn',
-    'train_sgd_minibatch',
-    'train_sgd_simple'
-    ]
-#imports
-import torch
-import warnings
-from collections import OrderedDict
-from sklearn import metrics
-import numpy as np
-import matplotlib.pyplot as plt
-import math
-
-#enable CUDA if possible
-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"):
-    if act_fn.lower()=="tanh":
-        k=OrderedDict([("in", torch.nn.Linear(in_dim,hidden_dim)), ('tanh0', torch.nn.Tanh())])
-        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())])
-        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())])
-        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())])
-        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:
-        warnings.warn("Did not specify a valid inner activation function. Returning nothing.")
-        return None
-
-    if end.lower()=="softmax":
-        k.update({"out": torch.nn.Linear(hidden_dim,out_dim), "softmax": torch.nn.Softmax()})
-    elif end.lower()=="none":
-        k.update({"out": torch.nn.Linear(hidden_dim,out_dim)})
-    elif end.lower()=="sigmoid":
-        k.update({"out": torch.nn.Linear(hidden_dim,out_dim), "sigmoid": torch.nn.Sigmoid()})
-    else:
-        warnings.warn("Did not specify a valid final activation function. Returning nothing.")
-        return None
-    
-    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"):
-    model=net.to(device)
-    data=data.to(device)
-    ground=ground.to(device)
-    if dev != None:
-        dev=dev.to(device)
-    losses=[]
-    dev_losses=[]
-    if loss.lower()=="mse":
-        loss_fn = torch.nn.MSELoss(reduction='sum')
-    elif loss.lower()=="cross entropy":
-        loss_fn = torch.nn.CrossEntropyLoss()
-    elif loss.lower()=="nll":
-        loss_fn = torch.nn.NLLLoss()
-    elif loss.lower()=="poisson nll":
-        loss_fn = torch.nn.PoissonNLLLoss()
-    else: 
-        warnings.warn("Did not specify a valid loss function. Returning nothing.")
-        return None
-    optimizer=torch.optim.SGD(model.parameters(), lr=learnrate)
-    for i in range(iters):
-        if i%testevery==0:
-            with torch.no_grad():
-                output = model(data)
-                ap = metrics.average_precision_score(ground.numpy(), output.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)
-                    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")
-                if graphsaveloc != None:
-                    plt.savefig(graphsaveloc+".pdf")
-        with torch.enable_grad():
-            optimizer.zero_grad()
-            output = model(data)
-            loss = loss_fn(output, ground)
-            loss.backward()
-            optimizer.step()
-    if modelsaveloc != None:
-        torch.save(model, modelsaveloc)
-    plt.show()
-    return model
-
-#train_sgd_minibatch: same as above, but with minibatches
-def train_sgd_minibatch(net, data, ground, dev=None, devg=None, epoch=100, batchsize=20, learnrate=1e-4, testevery=20, graphsaveloc=None, modelsaveloc=None, loss="mse"):
-    model=net.to(device)
-    data=data.to(device)
-    ground=ground.to(device)
-    if dev != None:
-        dev=dev.to(device)
-    losses=[]
-    dev_losses=[]
-    if loss.lower()=="mse":
-        loss_fn = torch.nn.MSELoss(reduction='sum')
-    elif loss.lower()=="cross entropy":
-        loss_fn = torch.nn.CrossEntropyLoss()
-    elif loss.lower()=="nll":
-        loss_fn = torch.nn.NLLLoss()
-    elif loss.lower()=="poisson nll":
-        loss_fn = torch.nn.PoissonNLLLoss()
-    else: 
-        warnings.warn("Did not specify a valid loss function. Returning nothing.")
-        return None
-    optimizer=torch.optim.SGD(model.parameters(), lr=learnrate)
-    itercount=0
-    for i in range(epoch):
-        print("EPOCH "+str(i)+" OF "+str(epoch-1))
-        batches=math.ceil(data.size()[0].item()/batchsize)
-        for j in range(batches):
-            batchdata=[]
-            batchground=[]
-            for k in range(j*batchsize, min((j+1)*batchsize, data.size()[0].item()),1):
-                batchdata.append(data[k])
-                batchground.append(ground[k])
-            batchdata=torch.stack(batchdata)
-            batchground=torch.stack(batchground)
-            if itercount%testevery==0:
-                with torch.no_grad():
-                    output = model(data)
-                    ap = metrics.average_precision_score(ground.numpy(), output.numpy())
-                    losses.append(ap)
-                    print(str(i)+": "+str(ap))
-                    plt.plot(np.array(range(0,i+1,testevery)),np.array(losses))
-                    if dev != None:
-                        output = model(dev)
-                        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")
-                    if graphsaveloc != None:
-                        plt.savefig(graphsaveloc+".pdf")
-            with torch.enable_grad():
-                optimizer.zero_grad()
-                output = model(batchdata)
-                loss = loss_fn(output, ground)
-                loss.backward()
-                optimizer.step()
-            itercount +=1
-    if modelsaveloc != None:
-        torch.save(model, modelsaveloc)
-    plt.show()
-    return model
+#Titan Robotics Team 2022: ML Module
+#Written by Arthur Lu & Jacob Levine
+#Notes:
+#   this should be imported as a python module using 'import titanlearn'
+#   this should be included in the local directory or environment variable
+#   this module has not been optimized for multhreaded computing
+#   this module learns from its mistakes far faster than 2022's captains
+#setup:
+
+__version__ = "1.0.0.001"
+
+#changelog should be viewed using print(analysis.__changelog__)
+__changelog__ = """changelog:
+1.0.0.xxx:
+    -added generation of ANNS, basic SGD training"""
+__author__ = (
+    "Arthur Lu <arthurlu@ttic.edu>, "
+    "Jacob Levine <jlevine@ttic.edu>,"
+    )
+__all__ = [
+    'linear_nn',
+    'train_sgd_minibatch',
+    'train_sgd_simple'
+    ]
+#imports
+import torch
+import warnings
+from collections import OrderedDict
+from sklearn import metrics, datasets
+import numpy as np
+import matplotlib.pyplot as plt
+import math
+
+#enable CUDA if possible
+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="none"):
+    if act_fn.lower()=="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))])
+        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))])
+        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))])
+        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:
+        warnings.warn("Did not specify a valid inner activation function. Returning nothing.")
+        return None
+
+    if end.lower()=="softmax":
+        k.update({"out": torch.nn.Linear(hidden_dim,out_dim), "softmax": torch.nn.Softmax()})
+    elif end.lower()=="none":
+        k.update({"out": torch.nn.Linear(hidden_dim,out_dim)})
+    elif end.lower()=="sigmoid":
+        k.update({"out": torch.nn.Linear(hidden_dim,out_dim), "sigmoid": torch.nn.Sigmoid()})
+    else:
+        warnings.warn("Did not specify a valid final activation function. Returning nothing.")
+        return None
+
+    return torch.nn.Sequential(k)
+
+#train_sgd_simple: trains network using SGD
+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)
+    if dev != None:
+        dev=dev.to(device)
+    losses=[]
+    dev_losses=[]
+    if loss.lower()=="mse":
+        loss_fn = torch.nn.MSELoss()
+    elif loss.lower()=="cross entropy":
+        loss_fn = torch.nn.CrossEntropyLoss()
+    elif loss.lower()=="nll":
+        loss_fn = torch.nn.NLLLoss()
+    elif loss.lower()=="poisson nll":
+        loss_fn = torch.nn.PoissonNLLLoss()
+    else:
+        warnings.warn("Did not specify a valid loss function. Returning nothing.")
+        return None
+    optimizer=torch.optim.SGD(model.parameters(), lr=learnrate)
+    for i in range(iters):
+        if i%testevery==0:
+            with torch.no_grad():
+                output = model(data)
+                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:
+        torch.save(model, modelsaveloc)
+    plt.show()
+    return model
+
+#train_sgd_minibatch: same as above, but with minibatches
+def train_sgd_minibatch(net, data, ground, dev=None, devg=None, epoch=100, batchsize=20, learnrate=1e-4, testevery=20, graphsaveloc=None, modelsaveloc=None, loss="mse"):
+    model=net.to(device)
+    data=data.to(device)
+    ground=ground.to(device)
+    if dev != None:
+        dev=dev.to(device)
+    losses=[]
+    dev_losses=[]
+    if loss.lower()=="mse":
+        loss_fn = torch.nn.MSELoss()
+    elif loss.lower()=="cross entropy":
+        loss_fn = torch.nn.CrossEntropyLoss()
+    elif loss.lower()=="nll":
+        loss_fn = torch.nn.NLLLoss()
+    elif loss.lower()=="poisson nll":
+        loss_fn = torch.nn.PoissonNLLLoss()
+    else:
+        warnings.warn("Did not specify a valid loss function. Returning nothing.")
+        return None
+    optimizer=torch.optim.LBFGS(model.parameters(), lr=learnrate)
+    itercount=0
+    for i in range(epoch):
+        print("EPOCH "+str(i)+" OF "+str(epoch-1))
+        batches=math.ceil(data.size()[0].item()/batchsize)
+        for j in range(batches):
+            batchdata=[]
+            batchground=[]
+            for k in range(j*batchsize, min((j+1)*batchsize, data.size()[0].item()),1):
+                batchdata.append(data[k])
+                batchground.append(ground[k])
+            batchdata=torch.stack(batchdata)
+            batchground=torch.stack(batchground)
+            if itercount%testevery==0:
+                with torch.no_grad():
+                    output = model(data)
+                    ap = metrics.average_precision_score(ground.numpy(), output.numpy())
+                    losses.append(ap)
+                    print(str(i)+": "+str(ap))
+                    plt.plot(np.array(range(0,i+1,testevery)),np.array(losses))
+                    if dev != None:
+                        output = model(dev)
+                        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")
+                    if graphsaveloc != None:
+                        plt.savefig(graphsaveloc+".pdf")
+            with torch.enable_grad():
+                optimizer.zero_grad()
+                output = model(batchdata)
+                loss = loss_fn(output, ground)
+                loss.backward()
+                optimizer.step()
+            itercount +=1
+    if modelsaveloc != None:
+        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()