updated pdf

merge mock conflict

.gitignore

@@ -0,0 +1,5 @@
+data/
+tmp/
+temp.*
+.venv/
+__pycache__/

HW01/HW1.md

@@ -12,13 +12,29 @@
 
 ## Problem II
 
+### Example 1: Boiling Water
+- **Forward Problem**: Given a specific amount of water, heat source, and initial temperature, find how long it takes the water to boil.  
+- **Inverse Problem**: Given the time it took for the water to boil, determine the starting temperature (or the heat source).  
+- **Difficulty**: The forward problem is easier as it involves direct calculations. The inverse problem is harder as it requires working backward to determine the unknowns.
+
+### Example 2: Hot Air Balloon
+- **Forward Problem**: Given the launch location, wind conditions, and balloon details, predict where the balloon will land.  
+- **Inverse Problem**: Given the landing location and flight path, determine the launch location and wind conditions.  
+- **Difficulty**: The forward problem is easier because it simulates the balloon's movement based on the given information. The inverse problem is harder because it requires determining the initial conditions (like launch location and wind) from the result.
+
+### Example 3: Wildfire
+- **Forward Problem**: Given the starting point of the fire, weather, and terrain, predict how the fire will spread.  
+- **Inverse Problem**: Given where the fire spread to, determine where and when it started.  
+- **Difficulty**: The forward problem is challenging as it involves predicting fire behavior under various factors. The inverse problem is even harder because it requires deducing the origin of the fire from its spread, which can be ambiguous and unclear.
+
+
 
 ## Problem III (Git and GitHub)
 ### Part I
 see https://github.com/ION606/COGMOD-HWI
 
 ### Part II
-see COMMIT_HASH_HERE
+see https://github.com/ION606/COGMOD-HWI/commit/260b0f4c3b9430a9d0ba2e29fd50d3f9c640c498
 
 ### Part III
 

HW01/mergeconflictfile.txt

@@ -1,2 +1,3 @@
-this is some text...yippie
+changing on a branch...dooodododooo
-bottom text
+oh no!
+I changed a file!

HW02/calculateHW2P5.py

@@ -0,0 +1,67 @@
+import numpy as np
+
+# SIMULATION CALCULATIONS
+def simulate_culprit(N, pSuperman):
+    return np.random.rand(N) < pSuperman
+
+def simulate_crumbs(N, supermanProb, batmanProb, culprit):
+    randomDraw = np.random.rand(N, 3)
+    supermanCrumbs = (randomDraw < supermanProb)
+    batmanCrumbs = (randomDraw < batmanProb)
+    return np.where(culprit[:, None], supermanCrumbs, batmanCrumbs)
+
+def combination(crumbResults, culprit):
+    combinations = {}
+    for couch in [False, True]:
+        for kitchen in [False, True]:
+            for gym in [False, True]:
+                for culprit_label, culprit_val in [("Superman", True), ("Batman", False)]:
+                    mask = (crumbResults[:, 0] == couch) & (crumbResults[:, 1] == kitchen) & (crumbResults[:, 2] == gym) & (culprit == culprit_val)
+                    combinations[(couch, kitchen, gym, culprit_label)] = np.sum(mask)
+    return combinations
+
+def print_probabilities(combinations, N):
+    print("Couch:\t Kitchen:  Gym:\t  Culprit:")
+    for k, v in combinations.items():
+        couch, kitchen, gym, culprit_label = k
+        #formatting
+        couch_str = 'True ' if couch else 'False'
+        kitchen_str = 'True ' if kitchen else 'False'
+        gym_str = 'True ' if gym else 'False'
+        if culprit_label == 'Batman': culprit_label = 'Batman  '
+
+        print(f"{couch_str}\t {kitchen_str}\t   {gym_str}  {culprit_label}: {(v / N) * 100:.2f}%")
+
+# ANALYTIC CALCULATIONS
+def analytic_probabilities(pSuperman, pBatman, supermanProb, batmanProb):
+    combinations = {}
+    for couch in [False, True]:
+        for kitchen in [False, True]:
+            for gym in [False, True]:
+                #Superman
+                prob_superman = pSuperman
+                prob_superman *= supermanProb[0] if couch else (1 - supermanProb[0])
+                prob_superman *= supermanProb[1] if kitchen else (1 - supermanProb[1])
+                prob_superman *= supermanProb[2] if gym else (1 - supermanProb[2])
+                combinations[(couch, kitchen, gym, "Superman")] = prob_superman
+
+                #Batman
+                prob_batman = pBatman
+                prob_batman *= batmanProb[0] if couch else (1 - batmanProb[0])
+                prob_batman *= batmanProb[1] if kitchen else (1 - batmanProb[1])
+                prob_batman *= batmanProb[2] if gym else (1 - batmanProb[2])
+                combinations[(couch, kitchen, gym, "Batman")] = prob_batman
+
+    return combinations
+
+def print_analytic_probabilities(combinations):
+    print("Couch:\t Kitchen:  Gym:\t  Culprit:")
+    for k, v in combinations.items():
+        couch, kitchen, gym, culprit_label = k
+
+        #formatting
+        couch_str = 'True ' if couch else 'False'
+        kitchen_str = 'True ' if kitchen else 'False'
+        gym_str = 'True ' if gym else 'False'
+        if culprit_label == 'Batman': culprit_label = 'Batman  '
+        print(f"{couch_str}\t {kitchen_str}\t   {gym_str}  {culprit_label}: {v * 100:.2f}%")

HW02/mainHW2P5.py

@@ -0,0 +1,41 @@
+from calculateHW2P5 import simulate_culprit, simulate_crumbs, combination, print_probabilities, analytic_probabilities, print_analytic_probabilities
+import numpy as np
+
+if __name__ == "__main__":
+    #N = 100000 #used while testing
+    NSize = [1000, 10000, 100000]
+    #Priors for each suspect
+    pSuperman = 0.5
+    pBatman = 0.5
+
+    #Likelihoods of crumbs on each location
+    supermanProb = np.array([0.3, 0.7, 0.2])
+    batmanProb = np.array([0.4, 0.6, 0.3])
+
+    #Simulate
+    '''
+    culprit = simulate_culprit(N, pSuperman)
+    crumbResults = simulate_crumbs(N, supermanProb, batmanProb, culprit)
+
+    print("Simulation:")
+    print_probabilities(combination(crumbResults, culprit), N)
+    '''
+    print("Simulation:")
+    for N in NSize:
+        print(f"N = {N}")
+        # Simulate culprit and crumbs
+        culprit = simulate_culprit(N, pSuperman)
+        crumbResults = simulate_crumbs(N, supermanProb, batmanProb, culprit)
+
+        # Count combinations and print probabilities
+        print("Simulated Probabilities:")
+        print_probabilities(combination(crumbResults, culprit), N)
+        print("\n")
+
+    #Analytic
+    print("Analytic:")
+    print_analytic_probabilities(analytic_probabilities(pSuperman, pBatman, supermanProb, batmanProb))
+
+'''
+As the value of N increases, the simulated probabilities get closer to the analytic probabilities. 
+'''

HW04/multiple_regression.stan

@@ -0,0 +1,28 @@
+data {
+  int<lower=0> N;             // Number of training samples
+  int<lower=0> M;             // Number of predictors (3: bmi, age, children)
+  matrix[N, M] X;             // Training predictors
+  vector[N] y;                // Training target (charges)
+  int<lower=0> N_test;        // Number of test samples
+  matrix[N_test, M] X_test;   // Test predictors
+}
+parameters {
+  real alpha;                 // Intercept
+  vector[M] beta;             // Regression coefficients
+  real<lower=0> sigma;        // Noise term
+}
+model {
+  // Priors
+  sigma ~ inv_gamma(2, 2);
+  alpha ~ normal(0, 10);
+  beta ~ normal(0, 1);
+
+  // Likelihood
+  y ~ normal(alpha + X * beta, sigma);
+}
+generated quantities {
+  vector[N_test] y_pred;      
+  for (i in 1:N_test) {
+    y_pred[i] = normal_rng(alpha + X_test[i] * beta, sigma);
+  }
+}

HW3/.gitignore

@@ -0,0 +1,2 @@
+build/
+.venv/

HW3/Q2/Q2_part1.py

@@ -0,0 +1,50 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+def simulate_ddm(v, a=1.0, beta=0.5, tau=0.3, sigma=1.0, dt=0.001, max_steps=3000):
+    X = beta * a  # start position
+    t = 0.0
+    for _ in range(max_steps):
+        dW = np.random.normal(0, np.sqrt(dt))
+        dX = v * dt + sigma * dW
+        X += dX
+        t += dt
+        if X >= a:
+            return t + tau, 1  # upper bound hit
+        elif X <= 0:
+            return t + tau, 0  # lower bound hit
+    return max_steps * dt + tau, None  # Timeout (optional)
+
+
+# terrible params (upped in part 2)
+vs = np.linspace(0.5, 1.5, 25)  # drift rates for test
+n_trials = 2000
+
+# store
+upper_means, lower_means = [], []
+
+for v in vs:
+    upper_rts, lower_rts = [], []
+    for _ in range(n_trials):
+        rt, choice = simulate_ddm(v)
+        if choice == 1:
+            upper_rts.append(rt)
+        elif choice == 0:
+            lower_rts.append(rt)
+    # means (ignore cases where no hits)
+    upper_means.append(np.mean(upper_rts) if upper_rts else np.nan)
+    lower_means.append(np.mean(lower_rts) if lower_rts else np.nan)
+
+# plotting yay
+plt.figure(figsize=(10, 6))
+plt.plot(vs, upper_means, 'o-', label='Upper Boundary Mean RT')
+plt.plot(vs, lower_means, 's-', label='Lower Boundary Mean RT')
+plt.plot(vs, np.array(upper_means) - np.array(lower_means),
+         'd-', label='Mean Difference')
+plt.xlabel('Drift Rate (v)')
+plt.ylabel('Response Time (s)')
+plt.title('Effect of Drift Rate on RT Distributions')
+plt.legend()
+plt.grid(True)
+plt.savefig('part1.png')

HW3/Q2/Q2_part2.py

@@ -0,0 +1,104 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from multiprocessing import Pool, cpu_count
+from functools import partial
+
+
+def sim_ddm(v=1.0, a=1.0, beta=0.5, tau=0.3, sigma=1.0, dt=0.001, max_steps=3000):
+	X = beta * a  # start
+	t = 0.0
+	for _ in range(max_steps):
+		dW = np.random.normal(0, np.sqrt(dt))
+		dX = v * dt + sigma * dW
+		X += dX
+		t += dt
+		if X >= a:
+			return t + tau, 1  # upper bound hit
+		elif X <= 0:
+			return t + tau, 0  # lower bound hit
+	return max_steps * dt + tau, None  # timeout (which I ignored)
+
+
+def sim_param(param_name, param_value, n_trials=200000):
+	default_params = {'v': 1.0, 'a': 1.0,
+                   'beta': 0.5, 'tau': 0.3, 'sigma': 1.0}
+	params = default_params.copy()
+	params[param_name] = param_value
+	upper_rts, lower_rts = [], []
+	for _ in range(n_trials):
+		rt, choice = sim_ddm(**params)
+		if choice == 1:
+			upper_rts.append(rt)
+		elif choice == 0:
+			lower_rts.append(rt)
+	return (upper_rts, lower_rts)  # Return all RTs
+
+
+# deepseek-r1 wrote this to help parallelize my code (because for loops aren't cool when they're frying my laptop)
+def parallel_sim_param(param_name, param_values, n_trials):
+	worker = partial(sim_param,
+                  param_name, n_trials=n_trials)
+	with Pool(processes=cpu_count()) as pool:
+		results = pool.map(worker, param_values)
+	return results
+
+
+parameters = {
+	'v': np.linspace(0.5, 1.5, 25),
+	'a': np.linspace(0.5, 2.0, 25),
+	'beta': np.linspace(0.3, 0.7, 25),
+	'tau': np.linspace(0.1, 0.5, 25),
+}
+
+fig, axes = plt.subplots(4, 2, figsize=(15, 20))  # should this be (15, 15)?
+axes = axes.flatten()
+
+for i, (param, values) in enumerate(parameters.items()):
+	results = parallel_sim_param(param, values, n_trials=200000)
+
+	# no bootstrapping
+	means_upper, means_lower = [], []
+	stdev_upper, stdev_lower = [], []
+
+	for upper_rts, lower_rts in results:
+		mu_upper = np.mean(upper_rts) if upper_rts else np.nan
+		mu_lower = np.mean(lower_rts) if lower_rts else np.nan
+		std_upper = np.std(upper_rts) if upper_rts else np.nan
+		std_lower = np.std(lower_rts) if lower_rts else np.nan
+
+		means_upper.append(mu_upper)
+		means_lower.append(mu_lower)
+		stdev_upper.append(std_upper)
+		stdev_lower.append(std_lower)
+
+	# means
+	ax_mean = axes[2 * i]
+	ax_mean.plot(values, means_upper, 'o-', label='Upper Boundary Mean RT')
+	ax_mean.plot(values, means_lower, 's-', label='Lower Boundary Mean RT')
+	ax_mean.plot(values, np.subtract(means_upper, means_lower),
+	             'd-', label='Difference', color='red')
+	ax_mean.set_xlabel(param)
+	ax_mean.set_ylabel('Response Time (s)')
+	ax_mean.set_title(f'Effect of {param} on RT Means')
+	ax_mean.legend()
+	ax_mean.grid(True)
+
+	# STDDEV
+	ax_std = axes[2 * i + 1]
+	ax_std.plot(values, stdev_upper, 'o-', label='Upper Boundary Std RT')
+	ax_std.plot(values, stdev_lower, 's-', label='Lower Boundary Std RT')
+	ax_std.set_xlabel(param)
+	ax_std.set_ylabel('Standard Deviation (s)')
+	ax_std.set_title(f'Effect of {param} on RT Std Devs')
+	ax_std.legend()
+	ax_std.grid(True)
+
+	plt.tight_layout()
+	plt.savefig('part2.png')
+
+	# DEBUGGING
+	print(f"\nVARYING {param.upper()}:\n")
+	print(f"Means (Upper): {np.round(means_upper, 5)}")
+	print(f"Means (Lower): {np.round(means_lower, 5)}")
+	print(f"Std (Upper): {np.round(stdev_upper, 5)}")
+	print(f"Std (Lower): {np.round(stdev_lower, 5)}")

HW3/Q2/Q4/Q4.ipynb

@@ -0,0 +1,421 @@
+{
+	"cells": [
+		{
+			"cell_type": "code",
+			"execution_count": 52,
+			"metadata": {},
+			"outputs": [],
+			"source": [
+				"import numpy as np\n",
+				"import stan\n",
+				"import arviz as az\n",
+				"\n",
+				"# stupid stan problems\n",
+				"import nest_asyncio\n",
+				"nest_asyncio.apply()\n",
+				"\n",
+				"# true param\n",
+				"alpha_true = 2.3,\n",
+				"beta_true = 4.0,\n",
+				"sigma_true = 2.0,\n",
+				"N = 100\n",
+				"\n",
+				"# simulation\n",
+				"np.random.seed(42)\n",
+				"x = np.random.normal(size=N)\n",
+				"y = alpha_true + beta_true * x + sigma_true * np.random.normal(size=N)"
+			]
+		},
+		{
+			"cell_type": "code",
+			"execution_count": 53,
+			"metadata": {},
+			"outputs": [],
+			"source": [
+				"stanCode = \"\"\"\n",
+				"data {\n",
+				"  int<lower=0> N;\n",
+				"  vector[N] x;\n",
+				"  vector[N] y;\n",
+				"}\n",
+				"parameters {\n",
+				"  real alpha;\n",
+				"  real beta;\n",
+				"  real<lower=0> sigma_sq;\n",
+				"}\n",
+				"transformed parameters {\n",
+				"  real<lower=0> sigma = sqrt(sigma_sq);\n",
+				"}\n",
+				"model {\n",
+				"  sigma_sq ~ inv_gamma(1, 1);  // prior on variance\n",
+				"  alpha ~ normal(0, 10);\n",
+				"  beta ~ normal(0, 10);\n",
+				"  y ~ normal(alpha + beta * x, sigma);  // likelihood\n",
+				"}\n",
+				"\"\"\""
+			]
+		},
+		{
+			"cell_type": "code",
+			"execution_count": 54,
+			"metadata": {},
+			"outputs": [
+				{
+					"name": "stdout",
+					"output_type": "stream",
+					"text": [
+						"Building...\n"
+					]
+				},
+				{
+					"name": "stderr",
+					"output_type": "stream",
+					"text": [
+						"\n",
+						"Building: found in cache, done.Sampling:   0%\n",
+						"Sampling:  25% (3000/12000)\n",
+						"Sampling:  50% (6000/12000)\n",
+						"Sampling:  75% (9000/12000)\n",
+						"Sampling: 100% (12000/12000)\n",
+						"Sampling: 100% (12000/12000), done.\n",
+						"Messages received during sampling:\n",
+						"  Gradient evaluation took 1.7e-05 seconds\n",
+						"  1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.\n",
+						"  Adjust your expectations accordingly!\n",
+						"  Gradient evaluation took 2.7e-05 seconds\n",
+						"  1000 transitions using 10 leapfrog steps per transition would take 0.27 seconds.\n",
+						"  Adjust your expectations accordingly!\n",
+						"  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
+						"  Exception: normal_lpdf: Scale parameter is 0, but must be positive! (in '/tmp/httpstan__2qigylb/model_74j73ceb.stan', line 19, column 2 to column 38)\n",
+						"  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
+						"  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
+						"  Gradient evaluation took 2e-05 seconds\n",
+						"  1000 transitions using 10 leapfrog steps per transition would take 0.2 seconds.\n",
+						"  Adjust your expectations accordingly!\n",
+						"  Gradient evaluation took 1e-05 seconds\n",
+						"  1000 transitions using 10 leapfrog steps per transition would take 0.1 seconds.\n",
+						"  Adjust your expectations accordingly!\n"
+					]
+				},
+				{
+					"data": {
+						"text/html": [
+							"<div>\n",
+							"<style scoped>\n",
+							"    .dataframe tbody tr th:only-of-type {\n",
+							"        vertical-align: middle;\n",
+							"    }\n",
+							"\n",
+							"    .dataframe tbody tr th {\n",
+							"        vertical-align: top;\n",
+							"    }\n",
+							"\n",
+							"    .dataframe thead th {\n",
+							"        text-align: right;\n",
+							"    }\n",
+							"</style>\n",
+							"<table border=\"1\" class=\"dataframe\">\n",
+							"  <thead>\n",
+							"    <tr style=\"text-align: right;\">\n",
+							"      <th></th>\n",
+							"      <th>mean</th>\n",
+							"      <th>sd</th>\n",
+							"      <th>hdi_3%</th>\n",
+							"      <th>hdi_97%</th>\n",
+							"      <th>mcse_mean</th>\n",
+							"      <th>mcse_sd</th>\n",
+							"      <th>ess_bulk</th>\n",
+							"      <th>ess_tail</th>\n",
+							"      <th>r_hat</th>\n",
+							"    </tr>\n",
+							"  </thead>\n",
+							"  <tbody>\n",
+							"    <tr>\n",
+							"      <th>alpha</th>\n",
+							"      <td>2.317</td>\n",
+							"      <td>0.192</td>\n",
+							"      <td>1.959</td>\n",
+							"      <td>2.683</td>\n",
+							"      <td>0.002</td>\n",
+							"      <td>0.002</td>\n",
+							"      <td>6909.0</td>\n",
+							"      <td>5804.0</td>\n",
+							"      <td>1.0</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>beta</th>\n",
+							"      <td>3.713</td>\n",
+							"      <td>0.208</td>\n",
+							"      <td>3.327</td>\n",
+							"      <td>4.117</td>\n",
+							"      <td>0.002</td>\n",
+							"      <td>0.002</td>\n",
+							"      <td>7805.0</td>\n",
+							"      <td>5904.0</td>\n",
+							"      <td>1.0</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>sigma_sq</th>\n",
+							"      <td>3.615</td>\n",
+							"      <td>0.511</td>\n",
+							"      <td>2.716</td>\n",
+							"      <td>4.584</td>\n",
+							"      <td>0.006</td>\n",
+							"      <td>0.006</td>\n",
+							"      <td>7166.0</td>\n",
+							"      <td>5819.0</td>\n",
+							"      <td>1.0</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>sigma</th>\n",
+							"      <td>1.897</td>\n",
+							"      <td>0.133</td>\n",
+							"      <td>1.648</td>\n",
+							"      <td>2.141</td>\n",
+							"      <td>0.002</td>\n",
+							"      <td>0.001</td>\n",
+							"      <td>7166.0</td>\n",
+							"      <td>5819.0</td>\n",
+							"      <td>1.0</td>\n",
+							"    </tr>\n",
+							"  </tbody>\n",
+							"</table>\n",
+							"</div>"
+						],
+						"text/plain": [
+							"           mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  \\\n",
+							"alpha     2.317  0.192   1.959    2.683      0.002    0.002    6909.0   \n",
+							"beta      3.713  0.208   3.327    4.117      0.002    0.002    7805.0   \n",
+							"sigma_sq  3.615  0.511   2.716    4.584      0.006    0.006    7166.0   \n",
+							"sigma     1.897  0.133   1.648    2.141      0.002    0.001    7166.0   \n",
+							"\n",
+							"          ess_tail  r_hat  \n",
+							"alpha       5804.0    1.0  \n",
+							"beta        5904.0    1.0  \n",
+							"sigma_sq    5819.0    1.0  \n",
+							"sigma       5819.0    1.0  "
+						]
+					},
+					"execution_count": 54,
+					"metadata": {},
+					"output_type": "execute_result"
+				}
+			],
+			"source": [
+				"# Define data first\n",
+				"data = {\"N\": N, \"x\": x, \"y\": y}\n",
+				"\n",
+				"# Build the model with data\n",
+				"model = stan.build(stanCode, data=data)\n",
+				"\n",
+				"# Sample\n",
+				"fit = model.sample(num_chains=4, num_samples=2000)\n",
+				"\n",
+				"az.summary(az.from_pystan(fit))"
+			]
+		},
+		{
+			"cell_type": "markdown",
+			"metadata": {},
+			"source": [
+				"### Step 4: Analyze Results for N=100\n",
+				"\n",
+				"Posterior summaries should be close to the true values:\n",
+				"\n",
+				"- **α**: approximately 2.3\n",
+				"- **β**: approximately 4.0\n",
+				"- **σ**: approximately 2.0\n",
+				"\n",
+				"Also compute the 95% credible intervals."
+			]
+		},
+		{
+			"cell_type": "markdown",
+			"metadata": {},
+			"source": [
+				"### Step 5: Repeat with N=1000\n",
+				"\n",
+				"Increase the sample size and rerun the simulation and model fitting."
+			]
+		},
+		{
+			"cell_type": "code",
+			"execution_count": 55,
+			"metadata": {},
+			"outputs": [
+				{
+					"name": "stdout",
+					"output_type": "stream",
+					"text": [
+						"Building...\n"
+					]
+				},
+				{
+					"name": "stderr",
+					"output_type": "stream",
+					"text": [
+						"\n",
+						"Building: found in cache, done.Sampling:   0%\n",
+						"Sampling:  25% (3000/12000)\n",
+						"Sampling:  50% (6000/12000)\n",
+						"Sampling:  75% (9000/12000)\n",
+						"Sampling: 100% (12000/12000)\n",
+						"Sampling: 100% (12000/12000), done.\n",
+						"Messages received during sampling:\n",
+						"  Gradient evaluation took 0.000146 seconds\n",
+						"  1000 transitions using 10 leapfrog steps per transition would take 1.46 seconds.\n",
+						"  Adjust your expectations accordingly!\n",
+						"  Gradient evaluation took 0.000126 seconds\n",
+						"  1000 transitions using 10 leapfrog steps per transition would take 1.26 seconds.\n",
+						"  Adjust your expectations accordingly!\n",
+						"  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
+						"  Exception: normal_lpdf: Scale parameter is 0, but must be positive! (in '/tmp/httpstan__2qigylb/model_74j73ceb.stan', line 19, column 2 to column 38)\n",
+						"  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
+						"  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
+						"  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
+						"  Exception: normal_lpdf: Scale parameter is 0, but must be positive! (in '/tmp/httpstan__2qigylb/model_74j73ceb.stan', line 19, column 2 to column 38)\n",
+						"  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
+						"  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
+						"  Gradient evaluation took 0.000123 seconds\n",
+						"  1000 transitions using 10 leapfrog steps per transition would take 1.23 seconds.\n",
+						"  Adjust your expectations accordingly!\n",
+						"  Gradient evaluation took 0.000135 seconds\n",
+						"  1000 transitions using 10 leapfrog steps per transition would take 1.35 seconds.\n",
+						"  Adjust your expectations accordingly!\n"
+					]
+				},
+				{
+					"data": {
+						"text/html": [
+							"<div>\n",
+							"<style scoped>\n",
+							"    .dataframe tbody tr th:only-of-type {\n",
+							"        vertical-align: middle;\n",
+							"    }\n",
+							"\n",
+							"    .dataframe tbody tr th {\n",
+							"        vertical-align: top;\n",
+							"    }\n",
+							"\n",
+							"    .dataframe thead th {\n",
+							"        text-align: right;\n",
+							"    }\n",
+							"</style>\n",
+							"<table border=\"1\" class=\"dataframe\">\n",
+							"  <thead>\n",
+							"    <tr style=\"text-align: right;\">\n",
+							"      <th></th>\n",
+							"      <th>mean</th>\n",
+							"      <th>sd</th>\n",
+							"      <th>hdi_3%</th>\n",
+							"      <th>hdi_97%</th>\n",
+							"      <th>mcse_mean</th>\n",
+							"      <th>mcse_sd</th>\n",
+							"      <th>ess_bulk</th>\n",
+							"      <th>ess_tail</th>\n",
+							"      <th>r_hat</th>\n",
+							"    </tr>\n",
+							"  </thead>\n",
+							"  <tbody>\n",
+							"    <tr>\n",
+							"      <th>alpha</th>\n",
+							"      <td>2.366</td>\n",
+							"      <td>0.062</td>\n",
+							"      <td>2.253</td>\n",
+							"      <td>2.484</td>\n",
+							"      <td>0.001</td>\n",
+							"      <td>0.001</td>\n",
+							"      <td>7563.0</td>\n",
+							"      <td>5508.0</td>\n",
+							"      <td>1.0</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>beta</th>\n",
+							"      <td>3.929</td>\n",
+							"      <td>0.063</td>\n",
+							"      <td>3.814</td>\n",
+							"      <td>4.048</td>\n",
+							"      <td>0.001</td>\n",
+							"      <td>0.001</td>\n",
+							"      <td>8352.0</td>\n",
+							"      <td>5934.0</td>\n",
+							"      <td>1.0</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>sigma_sq</th>\n",
+							"      <td>3.895</td>\n",
+							"      <td>0.174</td>\n",
+							"      <td>3.588</td>\n",
+							"      <td>4.236</td>\n",
+							"      <td>0.002</td>\n",
+							"      <td>0.002</td>\n",
+							"      <td>8354.0</td>\n",
+							"      <td>6044.0</td>\n",
+							"      <td>1.0</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>sigma</th>\n",
+							"      <td>1.973</td>\n",
+							"      <td>0.044</td>\n",
+							"      <td>1.894</td>\n",
+							"      <td>2.058</td>\n",
+							"      <td>0.000</td>\n",
+							"      <td>0.000</td>\n",
+							"      <td>8354.0</td>\n",
+							"      <td>6044.0</td>\n",
+							"      <td>1.0</td>\n",
+							"    </tr>\n",
+							"  </tbody>\n",
+							"</table>\n",
+							"</div>"
+						],
+						"text/plain": [
+							"           mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  \\\n",
+							"alpha     2.366  0.062   2.253    2.484      0.001    0.001    7563.0   \n",
+							"beta      3.929  0.063   3.814    4.048      0.001    0.001    8352.0   \n",
+							"sigma_sq  3.895  0.174   3.588    4.236      0.002    0.002    8354.0   \n",
+							"sigma     1.973  0.044   1.894    2.058      0.000    0.000    8354.0   \n",
+							"\n",
+							"          ess_tail  r_hat  \n",
+							"alpha       5508.0    1.0  \n",
+							"beta        5934.0    1.0  \n",
+							"sigma_sq    6044.0    1.0  \n",
+							"sigma       6044.0    1.0  "
+						]
+					},
+					"execution_count": 55,
+					"metadata": {},
+					"output_type": "execute_result"
+				}
+			],
+			"source": [
+				"N_large = 1000;\n",
+				"x_large = np.random.normal(size=N_large);\n",
+				"y_large = alpha_true + beta_true * x_large + sigma_true * np.random.normal(size=N_large);\n",
+				"\n",
+				"# create new data dictionary\n",
+				"data_large = {\"N\": N_large, \"x\": x_large, \"y\": y_large};\n",
+				"model_large = stan.build(stanCode, data=data_large)\n",
+				"\n",
+				"# fit the model again\n",
+				"fit_large = model_large.sample(num_chains=4, num_samples=2000);\n",
+				"\n",
+				"# check diagnostics for larger data\n",
+				"az.summary(az.from_pystan(fit_large))\n"
+			]
+		}
+	],
+	"metadata": {
+		"kernelspec": {
+			"display_name": "Python 3",
+			"language": "python",
+			"name": "python3"
+		},
+		"language_info": {
+			"name": "python",
+			"version": "3.x"
+		}
+	},
+	"nbformat": 4,
+	"nbformat_minor": 2
+}

HW3/Q2/Q4/Q4.txt

@@ -0,0 +1,71 @@
+To solve Problem 5, follow these steps:
+
+### Step 1: Simulate Data
+
+### Step 2: Stan Model Code
+Write the Stan model (`bayesian_regression.stan`):
+```stan
+data {
+  int<lower=0> N;
+  vector[N] x;
+  vector[N] y;
+}
+parameters {
+  real alpha;
+  real beta;
+  real<lower=0> sigma_sq;
+}
+transformed parameters {
+  real<lower=0> sigma = sqrt(sigma_sq);
+}
+model {
+  sigma_sq ~ inv_gamma(1, 1); // Prior on variance
+  alpha ~ normal(0, 10);
+  beta ~ normal(0, 10);
+  y ~ normal(alpha + beta * x, sigma); // Likelihood
+}
+```
+
+### Step 3: Fit the Model and Check Diagnostics
+Use `pystan` or `cmdstanpy` to run the model. Check Rhat (≈1) and ESS (sufficiently large). For example:
+```python
+import cmdstanpy
+
+model = cmdstanpy.CmdStanModel(stan_file="bayesian_regression.stan")
+data = {"N": N, "x": x, "y": y}
+fit = model.sample(data=data, chains=4, iter_sampling=2000)
+
+# Check diagnostics
+print(fit.diagnose())
+```
+
+### Step 4: Analyze Results for N=100
+Posterior summaries:
+- **Posterior means** should be close to true values (α=2.3, β=4.0, σ=2.0).
+- **Uncertainty**: Compute 95% credible intervals. Example output:
+  - α: 2.1 ± 0.4 (1.7 to 2.5)
+  - β: 3.8 ± 0.5 (3.3 to 4.3)
+  - σ: 1.9 ± 0.2 (1.7 to 2.1)
+
+### Step 5: Repeat with N=1000
+Increase sample size and rerun:
+```python
+N_large = 1000
+x_large = np.random.normal(size=N_large)
+y_large = alpha_true + beta_true * x_large + sigma_true * np.random.normal(size=N_large)
+```
+Fit the model again. Results will show:
+- **Tighter credible intervals** (e.g., β: 3.95 ± 0.1).
+- Reduced posterior variance, indicating higher precision.
+
+### Key Observations:
+1. **Accuracy**: Posterior means align closely with true parameters.
+2. **Uncertainty**: Credible intervals narrow as \(N\) increases, reflecting reduced uncertainty.
+3. **Diagnostics**: Ensure Rhat ≈1 and sufficient ESS for reliable inferences.
+
+**Visualization**: Plot prior vs. posterior histograms for parameters (using tools like `arviz` or `seaborn`), showing posterior concentration around true values, especially for \(N=1000\).
+
+---
+
+**Answer for LMS Submission**  
+Implement the steps above, ensuring your write-up includes code snippets, diagnostic results, and graphical comparisons. Highlight the reduction in posterior variance when increasing \(N\), demonstrating the influence of data quantity on Bayesian inference.

HW3/Q3/Q3.py

@@ -0,0 +1,46 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+# H Y P E R P A R A M E T E R S
+mu_prior = 0      # prior mean
+sigma2_prior = 2   # prior variance (omega_0^2)
+sigma2_likelihood = 1  # likelihood variance (omega^2)
+n_samples = 1000000      # number of Monte Carlo samples
+
+# simulate θ ~ N(mu_0, omega_0^2) and y ~ N(θ, (omega)^2)
+theta = np.random.normal(mu_prior, np.sqrt(sigma2_prior), n_samples)
+y = np.random.normal(theta, np.sqrt(sigma2_likelihood))
+
+# posterior params for each y
+sigma2_posterior = 1 / (1 / sigma2_prior + 1 / sigma2_likelihood)
+mu_posterior = (mu_prior / sigma2_prior + y / sigma2_likelihood) * \
+    sigma2_posterior  # posterior mean
+
+# E[Var[θ|y]]
+expected_posterior_var = sigma2_posterior
+var_posterior_mean = np.var(mu_posterior)  # var[𝔼[θ|y]]
+prior_var = sigma2_prior                   # var[θ]
+
+# verify identity
+sum_terms = expected_posterior_var + var_posterior_mean
+
+print(f"Prior Variance (Var[θ]): {prior_var:.4f}")
+print(
+    f"Expected Posterior Variance (𝔼[Var[θ|y]]): {expected_posterior_var:.4f}")
+print(f"Variance of Posterior Mean (Var[𝔼[θ|y]]): {var_posterior_mean:.4f}")
+print(f"Sum of Terms: {sum_terms:.4f}")
+print(f"Identity Holds: {np.isclose(prior_var, sum_terms, atol=1e-3)}")
+
+# Plot posterior means and variances
+plt.figure(figsize=(10, 6))
+plt.hist(mu_posterior, bins=50, density=True,
+         alpha=0.6, label="Posterior Means")
+plt.axvline(mu_prior, color='r', linestyle='--', label="Prior Mean")
+plt.xlabel("Posterior Mean (𝔼[θ|y])")
+plt.ylabel("Density")
+plt.title("Distribution of Posterior Means vs. Prior")
+plt.legend()
+plt.grid(True)
+# plt.show()
+plt.savefig('part3.png')

HW3/Q5/Q5.tex

@@ -0,0 +1,76 @@
+As a culinary data scientist, you investigate how cooking time (\(x\)) affects the length of "massive ramen noodles" (\(y\)). Using Bayesian linear regression, you model the relationship to quantify expansion rates and uncertainty.
+
+\subsection*{Methods}
+\subsubsection*{Model Specification}
+The regression model is:
+\[
+y_n = \alpha + \beta x_n + \epsilon_n, \quad \epsilon_n \sim \mathcal{N}(0, \sigma^2)
+\]
+\begin{itemize}
+    \item \textbf{Priors}: 
+    \begin{align*}
+        \alpha &\sim \mathcal{N}(0, 10) \quad \text{(Intercept)} \\
+        \beta &\sim \mathcal{N}(0, 10) \quad \text{(Slope)} \\
+        \sigma^2 &\sim \text{Inv-Gamma}(1, 1) \quad \text{(Noise)}
+    \end{align*}
+\end{itemize}
+
+\subsubsection*{Data Simulation}
+Data was generated with:
+\begin{itemize}
+    \item True parameters: \(\alpha = 2.3\), \(\beta = 4.0\), \(\sigma = 2.0\)
+    \item \(N = 100\) observations, \(x \sim \mathcal{N}(0, 1)\), \(y = \alpha + \beta x + \mathcal{N}(0, \sigma^2)\)
+\end{itemize}
+
+\subsection*{Results}
+\subsubsection*{Posterior Estimates (\(N = 100\))}
+\begin{table}[h]
+    \centering
+    \begin{tabular}{@{}lccc@{}}
+        \toprule
+        Parameter & Posterior Mean & 95\% HDI & True Value \\
+        \midrule
+        \(\alpha\) (Intercept) & 2.31 & [1.94, 2.65] & 2.3 \\
+        \(\beta\) (Slope) & 3.71 & [3.32, 4.13] & 4.0 \\
+        \(\sigma\) (Noise) & 1.91 & [1.67, 2.18] & 2.0 \\
+        \bottomrule
+    \end{tabular}
+    \caption{Posterior summaries vs. true values. HDI = Highest Density Interval.}
+\end{table}
+
+\subsubsection*{Convergence Diagnostics}
+\begin{itemize}
+    \item \textbf{R-hat}: 1.0 for all parameters (ideal: \(\leq 1.01\)).
+    \item \textbf{ESS (Effective Sample Size)}: \(\alpha\): 6123, \(\beta\): 7356, \(\sigma\): 6362 (exceeding thresholds for reliability).
+\end{itemize}
+
+\begin{figure}[h]
+    \centering
+    \includegraphics[width=0.8\textwidth]{posterior_plots.png}
+    \caption{Posterior distributions for \(\alpha\), \(\beta\), and \(\sigma\). Dashed lines indicate true values.}
+\end{figure}
+
+\subsubsection*{Effect of Increased Data (\(N = 1000\), Hypothetical)}
+\begin{itemize}
+    \item Expected uncertainty reduction: Credible interval widths shrink by \(\sim 60\%\).
+    \item Posteriors concentrate tightly around true values (law of large numbers).
+\end{itemize}
+
+\subsection*{Discussion}
+\subsubsection*{Accuracy and Uncertainty}
+\begin{itemize}
+    \item With \(N = 100\), estimates align closely with ground truth (e.g., \(\beta = 3.71\) vs. true \(4.0\)), but credible intervals reflect residual uncertainty.
+    \item Noise (\(\sigma\)) slightly underestimated but within plausible range.
+\end{itemize}
+
+\subsubsection*{Model Insights}
+\begin{itemize}
+    \item Noodles expand by \(\sim 3.7\) units per second (\(\beta\)), validating the hypothesis.
+    \item Stan's MCMC sampler achieved excellent convergence (R-hat = 1.0, ESS > 5000).
+\end{itemize}
+
+\subsubsection*{Limitations}
+\begin{itemize}
+    \item Assumes linearity and normality; real-world noodle expansion may exhibit nonlinear dynamics.
+    \item Hyperparameters (e.g., \(\mathcal{N}(0, 10)\)) chosen for demonstration, not domain knowledge.
+\end{itemize}

HW3/Q6/diffusion_model.stan

@@ -0,0 +1,37 @@
+data {
+    int<lower=1> N;
+    array[N] real<lower=0> y;
+    array[N] int<lower=1, upper=2> condition;
+    array[N] int<lower=0, upper=1> choice;
+}
+
+parameters {
+    // Your code here
+}
+
+model {
+    // Priors
+    // Your code here
+
+    // Likelihood
+    for (n in 1:N) {
+        // Condition 1
+        if (condition[n] == 1) {
+            if (choice[n] == 1) {
+                 // Your code here
+            }
+            else {
+                 // Your code here
+            }
+        }
+        // Condition 2
+        if (condition[n] == 2) {
+            if (choice[n] == 1) {
+                // Your code here
+            }
+            else {
+                 // Your code here
+            }
+        }
+    }
+}

HW3/Q6/sample_response_times.csv

@@ -0,0 +1,301 @@
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HW3/requirements.txt

@@ -0,0 +1,215 @@
+alabaster==1.0.0
+annotated-types==0.7.0
+anyascii==0.3.2
+anyio==4.8.0
+apparmor==4.0.3
+application-utility==1.4.0
+arandr==0.1.11
+argcomplete==3.5.3
+arrow==1.3.0
+attrs==23.2.1.dev0
+autocommand==2.2.2
+Automat==22.10.0
+b2==4.3.0
+b2sdk==2.7.0
+Babel==2.15.0
+black==25.1.0
+breezy==3.3.9
+btrfsutil==6.13
+build==1.2.2
+CacheControl==0.14.2
+cachy==0.3.0
+certifi==2025.1.31
+cffi==1.17.1
+cfgv==3.4.0
+chardet==5.2.0
+charset-normalizer==3.4.1
+cleo==2.1.0
+click==8.1.8
+configobj==5.0.9
+constantly==23.10.4
+crashtest==0.4.1
+cryptography==44.0.2
+cssselect2==0.7.0
+Cython==0.29.37
+dbus-python==1.4.0
+deluge==2.1.1
+distlib==0.3.9
+distro==1.9.0
+docopt==0.6.2
+docutils==0.21.2
+dulwich==0.22.8
+ecdsa==0.19.0
+editables==0.5
+fastbencode==0.3.1
+fastjsonschema==2.21.1
+filelock==3.18.0
+findpython==0.6.3
+flit_core==3.11.0
+GeoIP==1.3.2
+gpg==1.24.2
+gufw==24.4.0
+h11==0.14.0
+hatch==1.14.0
+hatchling==1.27.0
+html5lib==1.1
+httpcore==1.0.7
+httplib2==0.22.0
+httpx==0.28.1
+hyperlink==21.0.0
+identify==2.6.9
+idna==3.10
+ifaddr==0.2.0
+imagesize==1.4.1
+importlib_metadata==7.2.1
+incremental==22.10.0
+inflect==7.5.0
+iniconfig==2.0.0
+installer==0.7.0
+jaraco.classes==3.4.0
+jaraco.collections==5.1.0
+jaraco.context==6.0.1
+jaraco.functools==4.1.0
+jaraco.text==4.0.0
+jeepney==0.9.0
+Jinja2==3.1.5
+jsonschema==4.23.0
+jsonschema-specifications==2024.10.1
+keyring==25.6.0
+lark==1.2.2
+launchpadlib==2.0.0
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+lazr.uri==1.0.6
+legacy-cgi==2.6.2
+lensfun==0.3.4
+LibAppArmor==4.0.3
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+lockfile==0.12.2
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+wxPython==4.2.2
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Project/Makefile

@@ -0,0 +1,12 @@
+all: run analyze doc
+
+run:
+	python main.py
+
+analyze:
+	python main.py --analyze
+
+doc:
+	pdflatex report.tex
+	pdflatex report.tex
+	pdflatex report.tex

Project/analysis/history_adam_aggressive_123.csv

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Project/analysis/history_adam_aggressive_42.csv

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Project/analysis/history_adam_none_123.csv

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Project/analysis/history_adam_none_42.csv

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Project/analysis/history_adam_none_999.csv

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Project/analysis/history_adam_standard_123.csv

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Project/analysis/history_adam_standard_42.csv

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Project/analysis/history_sgd_aggressive_123.csv

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Project/analysis/history_sgd_aggressive_42.csv

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Project/analysis/history_sgd_aggressive_999.csv

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Project/analysis/history_sgd_none_123.csv

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Project/analysis/history_sgd_none_42.csv

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Project/analysis/history_sgd_none_999.csv

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Project/analysis/history_sgd_standard_123.csv

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Project/analysis/history_sgd_standard_42.csv

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Project/analysis/history_sgd_standard_999.csv

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Project/analysis_results.csv

@@ -0,0 +1,37 @@
+seed,optimizer,augmentation,test_acc,robustness
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Project/combined_results.json

@@ -0,0 +1,398 @@
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+    "robustness": {
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+  {
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+    "robustness": {
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+  },
+  {
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+    "optimizer": "sgd",
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+    "test_acc": 0.6896,
+    "robustness": {
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+      "0.2": 0.6251,
+      "0.3": 0.5515
+    }
+  },
+  {
+    "seed": 999,
+    "optimizer": "sgd",
+    "augmentation": "aggressive",
+    "test_acc": 0.663,
+    "robustness": {
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+      "0.2": 0.6143,
+      "0.3": 0.5326
+    }
+  },
+  {
+    "seed": 999,
+    "optimizer": "adam",
+    "augmentation": "none",
+    "test_acc": 0.5189,
+    "robustness": {
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+      "0.2": 0.4787,
+      "0.3": 0.4115
+    }
+  },
+  {
+    "seed": 999,
+    "optimizer": "adam",
+    "augmentation": "standard",
+    "test_acc": 0.4934,
+    "robustness": {
+      "0.1": 0.4822,
+      "0.2": 0.4312,
+      "0.3": 0.34
+    }
+  },
+  {
+    "seed": 999,
+    "optimizer": "adam",
+    "augmentation": "aggressive",
+    "test_acc": 0.4039,
+    "robustness": {
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+      "0.2": 0.3814,
+      "0.3": 0.3183
+    }
+  }
+]

Project/download_data.py

@@ -0,0 +1,7 @@
+from torchvision.datasets import CIFAR10, CIFAR100
+import tensorflow_datasets as tfds
+
+ds10 = CIFAR10(root='data/', train=True, download=True)
+ds100 = CIFAR100(root='data/', train=True, download=True)
+
+ds_c10c = tfds.load('cifar10_corrupted')

Project/main.py

@@ -0,0 +1,284 @@
+import os
+import argparse
+from itertools import product
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import DataLoader
+import torchvision
+from torchvision import transforms
+
+import json
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import statsmodels.api as sm
+from statsmodels.formula.api import ols
+from argparse import Namespace
+
+# simple cnn model definition
+# I looked a lot at https://github.com/giusarno/SimpleCNN/blob/master/examples/cifar10/themodel.py
+# before making this class, mostly because I was not aware of the `MaxPool2d` function
+
+
+class SimpleCNN(nn.Module):
+	def __init__(self, num_classes=10):
+		super(SimpleCNN, self).__init__()
+		DROPOUT_RATE = 0.2
+
+		self.features = nn.Sequential(
+			nn.Conv2d(3, 32, 3, padding=1), nn.ReLU(),
+			nn.MaxPool2d(2),
+			nn.Conv2d(32, 64, 3, padding=1), nn.ReLU(),
+			nn.MaxPool2d(2),
+		)
+
+		self.classifier = nn.Sequential(
+			nn.Flatten(),
+			nn.Dropout(DROPOUT_RATE),
+			nn.Linear(64 * 8 * 8, 128), nn.ReLU(),
+			nn.Linear(128, num_classes),
+		)
+
+	def forward(self, x):
+		x = self.features(x)
+		x = self.classifier(x)
+		return x
+
+
+def get_data_loaders(batch_size, augmentation):
+	# transform pipelines
+	if augmentation == 'none':
+		transform_train = transforms.Compose([
+			transforms.ToTensor(),
+		])
+	elif augmentation == 'standard':
+		transform_train = transforms.Compose([
+			transforms.RandomHorizontalFlip(),
+			transforms.RandomCrop(32, padding=4),
+			transforms.ToTensor(),
+		])
+	elif augmentation == 'aggressive':
+		transform_train = transforms.Compose([
+			transforms.RandomHorizontalFlip(),
+			transforms.RandomRotation(15),
+			transforms.RandomCrop(32, padding=4),
+			transforms.ColorJitter(brightness=0.2, contrast=0.2,
+								   saturation=0.2, hue=0.1),
+			transforms.ToTensor(),
+		])
+	else:
+		raise ValueError(f"unknown augmentation: {augmentation}")
+
+	transform_test = transforms.Compose([
+		transforms.ToTensor(),
+	])
+
+	train_dataset = torchvision.datasets.CIFAR10(
+		root='./data', train=True, download=True, transform=transform_train)
+	test_dataset = torchvision.datasets.CIFAR10(
+		root='./data', train=False, download=True, transform=transform_test)
+
+	train_loader = DataLoader(
+		train_dataset, batch_size=batch_size, shuffle=True, num_workers=4)
+	test_loader = DataLoader(
+		test_dataset, batch_size=batch_size, shuffle=False, num_workers=4)
+
+	return train_loader, test_loader
+
+
+# train for 1 epoch
+def train_one_epoch(model, optimizer, criterion, dataloader, device, aug=True):
+	model.train()
+	running_loss = 0.0
+	correct = 0
+	total = 0
+
+	for inputs, targets in dataloader:
+		inputs, targets = inputs.to(device), targets.to(device)
+		optimizer.zero_grad()
+
+		if aug:
+			noisstd = np.random.uniform(0, 0.2)
+			inputs = inputs + noisstd * torch.randn_like(inputs)
+			# inputs = torch.clamp(inputs, 0.0, 1.0)
+
+		outputs = model(inputs)
+		loss = criterion(outputs, targets)
+		loss.backward()
+		optimizer.step()
+
+		running_loss += loss.item() * inputs.size(0)
+		_, predicted = outputs.max(1)
+		correct += predicted.eq(targets).sum().item()
+		total += targets.size(0)
+
+	epoch_loss = running_loss / total
+	epoch_acc = correct / total
+	return epoch_loss, epoch_acc
+
+
+# eval on clean data
+def evaluate(model, criterion, dataloader, device):
+	model.eval()
+	running_loss = 0.0
+	correct = 0
+	total = 0
+	with torch.no_grad():
+		for inputs, targets in dataloader:
+			inputs, targets = inputs.to(device), targets.to(device)
+			outputs = model(inputs)
+			loss = criterion(outputs, targets)
+
+			running_loss += loss.item() * inputs.size(0)
+			_, predicted = outputs.max(1)
+			correct += predicted.eq(targets).sum().item()
+			total += targets.size(0)
+	loss = running_loss / total
+	acc = correct / total
+	return loss, acc
+
+
+# eval robustness under gaussian noise
+def evaluate_robustness(model, dataloader, device, noise_std):
+	model.eval()
+	correct = 0
+	total = 0
+	with torch.no_grad():
+		for inputs, targets in dataloader:
+			noisy_inputs = inputs + noise_std * torch.randn_like(inputs)
+			# noisy_inputs = torch.clamp(noisy_inputs, 0.0, 1.0)
+			noisy_inputs, targets = noisy_inputs.to(device), targets.to(device)
+
+			outputs = model(noisy_inputs)
+			_, predicted = outputs.max(1)
+			correct += predicted.eq(targets).sum().item()
+			total += targets.size(0)
+	acc = correct / total
+	return acc
+
+
+def analyze_results(results_path='results.json'):
+	with open(results_path) as f:
+		results = json.load(f)
+	df = pd.DataFrame(results)
+	df.to_csv('analysis_results.csv', index=False)
+
+	# full ANOVA w/interaction
+	model = ols('test_acc ~ C(optimizer) * C(augmentation)', data=df).fit()
+	anova_table = sm.stats.anova_lm(model, typ=2)
+	print('anova on test accuracy:')
+	print(anova_table)
+
+	# composite label
+	df['condition'] = df['optimizer'] + '_' + df['augmentation']
+ 
+	fig, ax = plt.subplots(figsize=(12, 8))
+	colors = plt.cm.viridis(np.linspace(0.2, 0.8, len(df)))
+	df.plot.bar(x='condition', y='test_acc', rot=45, color=colors, ax=ax)
+
+
+	df.plot.bar(x='condition', y='test_acc', rot=45)
+	plt.ylabel('test accuracy')
+	# plt.tight_layout()
+ 
+	# only show every other tick label to avoid overcrowding
+	tick_labels = ax.get_xticklabels()
+	new_labels = [label.get_text() if i % 2 == 0 else "" for i, label in enumerate(tick_labels)]
+	ax.set_xticklabels(new_labels)
+
+	
+	# ripped off the py docs --> viridis colormap for bars
+	colors = plt.cm.viridis(np.linspace(0.2, 0.8, len(df)))
+	ax = df.plot.bar(x='condition', y='test_acc', rot=45, color=colors)
+	plt.ylabel('test accuracy')
+	plt.tight_layout()
+	plt.savefig('test_acc_comparison.png')
+	print('saved plot to test_acc_comparison.png')
+
+
+def run_experiments(args):
+	device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+	results = []
+
+	optimizers = {
+		'sgd': lambda params: optim.SGD(params, lr=args.lr, momentum=0.9, weight_decay=1e-3),
+		'adam': lambda params: optim.Adam(params, lr=args.lr, weight_decay=1e-3)
+	}
+	augmentations = ['none', 'standard', 'aggressive']
+
+	seeds = [42, 123, 999]
+	for seed in seeds:
+		print("SEED", seed)
+		torch.manual_seed(seed)
+		np.random.seed(seed)
+
+		for opt_name in optimizers:
+			for aug in augmentations:
+				train_loader, test_loader = get_data_loaders(args.batch_size, aug)
+				noise_levels = [0.1, 0.2, 0.3]
+				model = SimpleCNN(num_classes=10).to(device)
+				optimizer = optimizers[opt_name](model.parameters())
+				criterion = nn.CrossEntropyLoss()
+				history = {
+					'epoch': [], 'train_loss': [], 'train_acc': [],
+					'test_loss': [], 'test_acc': []
+				}
+
+				for epoch in range(args.epochs):
+					train_loss, train_acc = train_one_epoch(
+						model, optimizer, criterion, train_loader, device)
+
+					test_loss, test_acc = evaluate(
+						model, criterion, test_loader, device)
+
+					history['epoch'].append(epoch + 1)
+					history['train_loss'].append(train_loss)
+					history['train_acc'].append(train_acc)
+					history['test_loss'].append(test_loss)
+					history['test_acc'].append(test_acc)
+
+					print(f"[{opt_name}][{aug}][epoch {epoch + 1}] "
+											f"train_loss={train_loss:.4f}, train_acc={train_acc:.4f}, "
+											f"test_acc={test_acc:.4f}")
+
+				robustness = {noise: evaluate_robustness(
+					model, test_loader, device, noise) for noise in noise_levels}
+
+				pd.DataFrame(history).to_csv(
+					f"analysis/history_{opt_name}_{aug}_{seed}.csv", index=False)
+
+				results.append({
+					'seed': seed,
+					'optimizer': opt_name,
+					'augmentation': aug,
+					'test_acc': test_acc,
+					'robustness': robustness
+				})
+
+	with open('results.json', 'w') as f:
+		json.dump(results, f, indent=2)
+	print('saved results to results.json')
+
+
+# credit: I gave chatgpt a list of args and it made the arg parser for me
+if __name__ == '__main__':
+	parser = argparse.ArgumentParser()
+
+	parser.add_argument('--batch_size', type=int, default=128)
+	parser.add_argument('--lr', type=float, default=0.01)
+	parser.add_argument('--epochs', type=int, default=20)
+	parser.add_argument('--analyze', action='store_true',
+						help='run analysis on results')
+
+	args = parser.parse_args()
+	print(json.dumps(vars(args)))
+	print(args)
+	# args = Namespace(batch_size=128, lr=0.01, epochs=20, analyze=False)
+	# exit(1)
+
+	if args.analyze:
+		analyze_results("combined_results.json")
+	else:
+		run_experiments(args)

Project/old/analysis_results.csv

@@ -0,0 +1,19 @@
+seed,optimizer,augmentation,test_acc,robustness
+42,sgd,none,0.7088,"{'0.1': 0.6319, '0.2': 0.4336, '0.3': 0.2913}"
+42,sgd,standard,0.6859,"{'0.1': 0.5952, '0.2': 0.4019, '0.3': 0.2757}"
+42,sgd,aggressive,0.6536,"{'0.1': 0.5778, '0.2': 0.43, '0.3': 0.2943}"
+42,adam,none,0.5451,"{'0.1': 0.4221, '0.2': 0.2298, '0.3': 0.1545}"
+42,adam,standard,0.5101,"{'0.1': 0.454, '0.2': 0.2098, '0.3': 0.1324}"
+42,adam,aggressive,0.4427,"{'0.1': 0.4048, '0.2': 0.2461, '0.3': 0.1547}"
+123,sgd,none,0.6974,"{'0.1': 0.63, '0.2': 0.4452, '0.3': 0.312}"
+123,sgd,standard,0.6674,"{'0.1': 0.6252, '0.2': 0.4146, '0.3': 0.2764}"
+123,sgd,aggressive,0.6691,"{'0.1': 0.6179, '0.2': 0.4691, '0.3': 0.3423}"
+123,adam,none,0.6049,"{'0.1': 0.4685, '0.2': 0.3387, '0.3': 0.2378}"
+123,adam,standard,0.4654,"{'0.1': 0.4071, '0.2': 0.3073, '0.3': 0.2341}"
+123,adam,aggressive,0.5096,"{'0.1': 0.4624, '0.2': 0.3219, '0.3': 0.2159}"
+999,sgd,none,0.7058,"{'0.1': 0.6252, '0.2': 0.3848, '0.3': 0.2276}"
+999,sgd,standard,0.6861,"{'0.1': 0.6002, '0.2': 0.4184, '0.3': 0.2986}"
+999,sgd,aggressive,0.6595,"{'0.1': 0.5775, '0.2': 0.4165, '0.3': 0.2899}"
+999,adam,none,0.5573,"{'0.1': 0.4562, '0.2': 0.293, '0.3': 0.2167}"
+999,adam,standard,0.4835,"{'0.1': 0.4136, '0.2': 0.2221, '0.3': 0.1548}"
+999,adam,aggressive,0.5123,"{'0.1': 0.449, '0.2': 0.2571, '0.3': 0.1658}"

Project/old/analytics/analysis_results.csv

@@ -0,0 +1,19 @@
+seed,optimizer,augmentation,test_acc,robustness
+42,sgd,none,0.7088,"{'0.1': 0.6319, '0.2': 0.4336, '0.3': 0.2913}"
+42,sgd,standard,0.6859,"{'0.1': 0.5952, '0.2': 0.4019, '0.3': 0.2757}"
+42,sgd,aggressive,0.6536,"{'0.1': 0.5778, '0.2': 0.43, '0.3': 0.2943}"
+42,adam,none,0.5451,"{'0.1': 0.4221, '0.2': 0.2298, '0.3': 0.1545}"
+42,adam,standard,0.5101,"{'0.1': 0.454, '0.2': 0.2098, '0.3': 0.1324}"
+42,adam,aggressive,0.4427,"{'0.1': 0.4048, '0.2': 0.2461, '0.3': 0.1547}"
+123,sgd,none,0.6974,"{'0.1': 0.63, '0.2': 0.4452, '0.3': 0.312}"
+123,sgd,standard,0.6674,"{'0.1': 0.6252, '0.2': 0.4146, '0.3': 0.2764}"
+123,sgd,aggressive,0.6691,"{'0.1': 0.6179, '0.2': 0.4691, '0.3': 0.3423}"
+123,adam,none,0.6049,"{'0.1': 0.4685, '0.2': 0.3387, '0.3': 0.2378}"
+123,adam,standard,0.4654,"{'0.1': 0.4071, '0.2': 0.3073, '0.3': 0.2341}"
+123,adam,aggressive,0.5096,"{'0.1': 0.4624, '0.2': 0.3219, '0.3': 0.2159}"
+999,sgd,none,0.7058,"{'0.1': 0.6252, '0.2': 0.3848, '0.3': 0.2276}"
+999,sgd,standard,0.6861,"{'0.1': 0.6002, '0.2': 0.4184, '0.3': 0.2986}"
+999,sgd,aggressive,0.6595,"{'0.1': 0.5775, '0.2': 0.4165, '0.3': 0.2899}"
+999,adam,none,0.5573,"{'0.1': 0.4562, '0.2': 0.293, '0.3': 0.2167}"
+999,adam,standard,0.4835,"{'0.1': 0.4136, '0.2': 0.2221, '0.3': 0.1548}"
+999,adam,aggressive,0.5123,"{'0.1': 0.449, '0.2': 0.2571, '0.3': 0.1658}"

Project/old/analytics/history_adam_aggressive_123.csv

@@ -0,0 +1,11 @@
+epoch,train_loss,train_acc,test_loss,test_acc
+1,1.8846732257843017,0.3103,1.5956477916717529,0.4208
+2,1.6917855532073975,0.38452,1.5259598985671996,0.439
+3,1.6328186359024048,0.40266,1.5264780994415283,0.4508
+4,1.5851124533462524,0.42526,1.424309602355957,0.4878
+5,1.5511348150634765,0.43632,1.4553828117370606,0.4854
+6,1.5411012967681885,0.44224,1.5167289329528808,0.4561
+7,1.5281506721115112,0.4473,1.3984178335189819,0.5015
+8,1.5113245751190185,0.4561,1.3988324977874755,0.5007
+9,1.5111655992889403,0.45554,1.4732477207183838,0.4733
+10,1.498042034225464,0.46026,1.3725576107025146,0.5096

Project/old/analytics/history_adam_aggressive_42.csv

@@ -0,0 +1,11 @@
+epoch,train_loss,train_acc,test_loss,test_acc
+1,2.0559358141326904,0.24472,1.828050934791565,0.3374
+2,1.857869116783142,0.3182,1.6779026025772095,0.3909
+3,1.7681750354385375,0.35418,1.6347509387969972,0.4094
+4,1.7275719815826416,0.37254,1.6000082025527953,0.4225
+5,1.699682864151001,0.37816,1.571951426887512,0.4362
+6,1.6953852003860475,0.38134,1.582085866355896,0.4284
+7,1.6721481609344482,0.392,1.584311390686035,0.4267
+8,1.6576726916885376,0.39738,1.5268918621063232,0.443
+9,1.6475247369384765,0.402,1.5393544744491576,0.4441
+10,1.6505113174819945,0.39736,1.5213500274658203,0.4427

Project/old/analytics/history_adam_aggressive_999.csv

@@ -0,0 +1,11 @@
+epoch,train_loss,train_acc,test_loss,test_acc
+1,1.9911173669052125,0.27896,1.6828551885604859,0.3994
+2,1.6908308898162843,0.38222,1.6040934282302857,0.4364
+3,1.6209180907440186,0.41298,1.4993294555664063,0.459
+4,1.591390087928772,0.42586,1.5083149290084839,0.4616
+5,1.5589105658340454,0.43662,1.428050018119812,0.4809
+6,1.540269641456604,0.44422,1.4299810447692871,0.4858
+7,1.5332854122543336,0.44906,1.3867164831161498,0.5119
+8,1.5187089794921875,0.4558,1.3682811828613282,0.5136
+9,1.5207957489013673,0.45552,1.4490770374298096,0.4736
+10,1.504308384437561,0.4596,1.3697486953735352,0.5123

Project/old/analytics/history_adam_none_123.csv

@@ -0,0 +1,11 @@
+epoch,train_loss,train_acc,test_loss,test_acc
+1,1.6660349139404298,0.39084,1.3631993772506714,0.5092
+2,1.306575980796814,0.53194,1.2523228994369506,0.5514
+3,1.189557453918457,0.57828,1.2570601411819458,0.5594
+4,1.122563935699463,0.60502,1.187407196044922,0.5902
+5,1.0689283304977417,0.6233,1.2376863445281983,0.5659
+6,1.0334041289138793,0.6345,1.170531530189514,0.592
+7,0.9959703926849365,0.64648,1.1732149269104004,0.5901
+8,0.9762340403366089,0.65428,1.185727474975586,0.5919
+9,0.962807445487976,0.65934,1.1849331123352052,0.5937
+10,0.9365197282409667,0.66622,1.1537712555885316,0.6049

Project/old/analytics/history_adam_none_42.csv

@@ -0,0 +1,11 @@
+epoch,train_loss,train_acc,test_loss,test_acc
+1,1.7286399437713622,0.37382,1.4246170207977296,0.4816
+2,1.3806899689483643,0.50334,1.3549695669174195,0.5164
+3,1.3021614087677003,0.53534,1.332729033279419,0.5202
+4,1.2415979095458984,0.55796,1.2883787483215332,0.542
+5,1.1996409232330323,0.5716,1.2885587697982788,0.5404
+6,1.1598682831573486,0.58466,1.2654446369171142,0.5521
+7,1.1435769744873048,0.59348,1.2620088846206665,0.5593
+8,1.122702364425659,0.60254,1.2513995946884156,0.5613
+9,1.0767982495117188,0.6175,1.2432810546875,0.5644
+10,1.0651884030914307,0.6178,1.321402802658081,0.5451

Project/old/analytics/history_adam_none_999.csv

@@ -0,0 +1,11 @@
+epoch,train_loss,train_acc,test_loss,test_acc
+1,1.7729383182525635,0.35124,1.5228908493041993,0.4427
+2,1.430595055770874,0.482,1.38000087184906,0.5018
+3,1.32311696308136,0.52478,1.313341820716858,0.5259
+4,1.2678979627990723,0.54632,1.2857267393112182,0.5393
+5,1.2276857889556885,0.56124,1.299703761291504,0.5393
+6,1.1998348657989502,0.57262,1.2608893209457397,0.5565
+7,1.1683492805480957,0.58382,1.27673601436615,0.5546
+8,1.157327294807434,0.5878,1.2376579500198364,0.5654
+9,1.131808783454895,0.59764,1.2557779052734375,0.5572
+10,1.123384162902832,0.60062,1.2491890354156494,0.5573

Project/old/analytics/history_adam_standard_123.csv

@@ -0,0 +1,11 @@
+epoch,train_loss,train_acc,test_loss,test_acc
+1,1.8813365224456786,0.30172,1.6416668697357177,0.3985
+2,1.6806913624954223,0.37672,1.5487596210479737,0.4274
+3,1.6351341115570068,0.40124,1.5625509996414184,0.4249
+4,1.6064803477859497,0.41056,1.511009596824646,0.4455
+5,1.5825084410476684,0.42064,1.5225824378967285,0.4431
+6,1.5676781223678589,0.42982,1.4784889535903931,0.4585
+7,1.5567302836608887,0.43112,1.4934770944595337,0.4557
+8,1.5449237117004395,0.43588,1.463665308380127,0.4715
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+10,1.5271776266098023,0.44406,1.4860247068405152,0.4654

Project/old/analytics/history_adam_standard_42.csv

@@ -0,0 +1,11 @@
+epoch,train_loss,train_acc,test_loss,test_acc
+1,1.9121077362060548,0.28534,1.6980935123443603,0.3717
+2,1.634671248512268,0.3994,1.5801765157699585,0.4126
+3,1.5572229358291625,0.43102,1.4792659259796141,0.4583
+4,1.5200784818267823,0.44598,1.4566459318161011,0.4732
+5,1.4923802797317505,0.4583,1.3978405960083007,0.4928
+6,1.4686766564178466,0.46806,1.3776366807937621,0.503
+7,1.4700688259124757,0.46884,1.3533947219848632,0.5163
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+10,1.4347680015182496,0.48004,1.3694934066772462,0.5101

Project/old/analytics/history_adam_standard_999.csv

@@ -0,0 +1,11 @@
+epoch,train_loss,train_acc,test_loss,test_acc
+1,1.8785975454711914,0.30716,1.543831322669983,0.4375
+2,1.6268331103515625,0.40026,1.5018853443145752,0.4532
+3,1.5749516596221924,0.42014,1.5034992160797118,0.452
+4,1.5602307732009888,0.42962,1.4366535945892334,0.477
+5,1.5448442478561402,0.43214,1.439453507232666,0.4691
+6,1.5286526789474488,0.43836,1.4527703874588012,0.4671
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+9,1.5096457154083252,0.44728,1.4133702894210816,0.4894
+10,1.495245510482788,0.45166,1.411236897277832,0.4835

Project/old/analytics/history_sgd_aggressive_123.csv

@@ -0,0 +1,11 @@
+epoch,train_loss,train_acc,test_loss,test_acc
+1,2.0651948305892947,0.24042,1.729777430343628,0.3836
+2,1.7303296995544433,0.37548,1.49167406539917,0.4667
+3,1.5553171773147583,0.4367,1.3456432037353516,0.5205
+4,1.4635125664901734,0.47256,1.2475262697219849,0.5611
+5,1.3722680527114868,0.50766,1.1914715614318847,0.5746
+6,1.3082861290740966,0.53284,1.121172977924347,0.5981
+7,1.2505797283554076,0.5567,1.1289072477340698,0.5922
+8,1.2003705361175536,0.57516,1.0124303802490235,0.6468
+9,1.1683612518310547,0.58702,0.9980959354400635,0.65
+10,1.1325882072067261,0.6023,0.9680170437812805,0.6691

Project/old/analytics/history_sgd_aggressive_42.csv

@@ -0,0 +1,11 @@
+epoch,train_loss,train_acc,test_loss,test_acc
+1,2.0705010092163088,0.22946,1.7674200216293334,0.3802
+2,1.7432165607452392,0.36844,1.533634359550476,0.4416
+3,1.5778629602050782,0.42886,1.3606201539993286,0.5097
+4,1.4700220097351073,0.46802,1.2655291788101197,0.5451
+5,1.3893862116241456,0.50128,1.204566476535797,0.5692
+6,1.3244363966751098,0.5255,1.2020204118728637,0.5744
+7,1.2688888247299195,0.54674,1.0509296971321105,0.6334
+8,1.2191224797821045,0.56592,1.0520767385482788,0.6286
+9,1.1705705532073976,0.58452,1.0169856172561647,0.6394
+10,1.1425927544021606,0.59578,0.9944705787658692,0.6536

Project/old/analytics/history_sgd_aggressive_999.csv

@@ -0,0 +1,11 @@
+epoch,train_loss,train_acc,test_loss,test_acc
+1,2.028081403198242,0.25312,1.7972938034057617,0.3576
+2,1.6919663692855835,0.38654,1.4519519706726074,0.4827
+3,1.5530475779342652,0.43992,1.3310353057861328,0.5251
+4,1.4540857498931885,0.47812,1.292456304550171,0.5413
+5,1.3654178101348877,0.51218,1.1448781085014343,0.6
+6,1.3064117618942261,0.53414,1.1193419974327088,0.6077
+7,1.2500248126220703,0.5551,1.0644649827957153,0.6217
+8,1.2079097275543214,0.5713,1.0305480089187622,0.6358
+9,1.1659785708236694,0.58452,0.9741595977783203,0.6535
+10,1.1451263586425782,0.59474,0.9785669965744018,0.6595

Project/old/analytics/history_sgd_none_123.csv

@@ -0,0 +1,11 @@
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Project/old/analytics/history_sgd_none_42.csv

@@ -0,0 +1,11 @@
+epoch,train_loss,train_acc,test_loss,test_acc
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Project/old/analytics/history_sgd_none_999.csv

@@ -0,0 +1,11 @@
+epoch,train_loss,train_acc,test_loss,test_acc
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Project/old/analytics/history_sgd_standard_123.csv

@@ -0,0 +1,11 @@
+epoch,train_loss,train_acc,test_loss,test_acc
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Project/old/analytics/history_sgd_standard_42.csv

@@ -0,0 +1,11 @@
+epoch,train_loss,train_acc,test_loss,test_acc
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Project/old/analytics/history_sgd_standard_999.csv

@@ -0,0 +1,11 @@
+epoch,train_loss,train_acc,test_loss,test_acc
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Project/old/output.txt

@@ -0,0 +1,60 @@
+[sgd][none][epoch 0] train_loss=1.9493, train_acc=0.2892, test_acc=0.4237
+[sgd][none][epoch 1] train_loss=1.4806, train_acc=0.4651, test_acc=0.5235
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+[sgd][none][epoch 3] train_loss=1.1722, train_acc=0.5809, test_acc=0.6119
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+[sgd][none][epoch 5] train_loss=0.9572, train_acc=0.6634, test_acc=0.6452
+[sgd][none][epoch 6] train_loss=0.8812, train_acc=0.6916, test_acc=0.6703
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+[sgd][none][epoch 9] train_loss=0.6798, train_acc=0.7641, test_acc=0.6843
+[sgd][standard][epoch 0] train_loss=2.0006, train_acc=0.2638, test_acc=0.4103
+[sgd][standard][epoch 1] train_loss=1.6251, train_acc=0.4074, test_acc=0.4890
+[sgd][standard][epoch 2] train_loss=1.4750, train_acc=0.4641, test_acc=0.5426
+[sgd][standard][epoch 3] train_loss=1.3654, train_acc=0.5054, test_acc=0.5678
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+[adam][aggressive][epoch 9] train_loss=1.5463, train_acc=0.4410, test_acc=0.4831

Project/old/results.json

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Project/old/results_2.json

@@ -0,0 +1,200 @@
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Project/old/results_old.json

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Project/on_CPU/analysis_results.csv

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Project/on_CPU/analytics/history_adam_aggressive_123.csv

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Project/on_CPU/analytics/history_adam_aggressive_42.csv

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Project/on_CPU/analytics/history_adam_aggressive_999.csv

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Project/on_CPU/analytics/history_adam_none_123.csv

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Project/on_CPU/analytics/history_adam_none_42.csv

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Project/on_CPU/analytics/history_adam_none_999.csv

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Project/on_CPU/analytics/history_adam_standard_123.csv

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Project/on_CPU/analytics/history_adam_standard_42.csv

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Project/on_CPU/analytics/history_adam_standard_999.csv

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Project/on_CPU/analytics/history_sgd_aggressive_123.csv

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Project/on_CPU/analytics/history_sgd_aggressive_42.csv

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Project/on_CPU/analytics/history_sgd_aggressive_999.csv

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