who knows if this is a good idea

assignment-2/assignment_2/GML.py

@@ -1,305 +0,0 @@
-## imports
-import numpy as np
-import pandas as pd
-from scipy.optimize import minimize
-from scipy.stats import norm
-import math
-
-
-## Problem 1
-data = [4, 5, 7, 8, 8, 9, 10, 5, 2, 3, 5, 4, 8, 9]
-
-data_mean = np.mean(data)
-data_variance = np.var(data)
-
-
-mu = 0.5
-sigma = 0.5
-w = np.array([mu, sigma])
-
-w_star = np.array([data_mean, data_variance])
-mu_star = data_mean
-sigma_star = np.sqrt(data_variance)
-offset = 10 * np.random.random(2)
-
-w1p = w_star + 0.5 * offset
-w1n = w_star - 0.5 * offset
-w2p = w_star + 0.25 * offset
-w2n = w_star - 0.25 * offset
-
-# Negative Log Likelihood is defined as follows:
-# $-\ln(\frac{1}{\sqrt{2\pi\sigma^2}}\exp(-\frac{1}{2}\frac{(x-\mu)}{\sigma}^2))$.
-# Ignoring the contribution of the constant, we find that $\frac{\delta}{\delta
-# \mu} \mathcal{N} = \frac{\mu-x}{\sigma^2}$ and $\frac{\delta}{\delta \sigma}
-# \mathcal{N} = \frac{\sigma^2 + (\mu-x)^2 - \sigma^2}{\sigma^3}$. We apply these as our step functions for our SGD.
-
-loss = lambda mu, sigma, x: np.sum(
-    [-np.log(norm.pdf(xi, loc=mu, scale=sigma)) for xi in x]
-)
-
-loss_2_electric_boogaloo = lambda mu, sigma, x: -len(x) / 2 * np.log(
-    2 * np.pi * sigma**2
-) - 1 / (2 * sigma**2) * np.sum((x - mu) ** 2)
-
-
-dmu = lambda mu, sigma, x: -np.sum([mu - xi for xi in x]) / (sigma**2)
-dsigma = lambda mu, sigma, x: -len(x) / sigma + np.sum([(mu - xi) ** 2 for xi in x]) / (
-    sigma**3
-)
-
-log = []
-
-
-def SGD_problem1(mu, sigma, x, learning_rate=0.01, n_epochs=1000):
-    global log
-    log = []
-    for epoch in range(n_epochs):
-        mu += learning_rate * dmu(mu, sigma, x)
-        sigma += learning_rate * dsigma(mu, sigma, x)
-
-        # print(f"Epoch {epoch}, Loss: {loss(mu, sigma, x)}, New mu: {mu}, New sigma: {sigma}")
-        log.append(
-            {
-                "Epoch": epoch,
-                "Loss": loss(mu, sigma, x),
-                "Loss 2 Alternative": loss_2_alternative(mu, sigma, x),
-                "New mu": mu,
-                "New sigma": sigma,
-            }
-        )
-    return np.array([mu, sigma])
-
-
-def debug_SGD_1(wnn, data):
-    print("SGD Problem 1")
-    print("wnn", SGD_problem1(*wnn, data))
-    dflog = pd.DataFrame(log)
-    dflog["mu_star"] = mu_star
-    dflog["mu_std"] = sigma_star
-    print(f"mu diff at start {dflog.iloc[0]['New mu'] - dflog.iloc[0]['mu_star']}")
-    print(f"mu diff at end {dflog.iloc[-1]['New mu'] - dflog.iloc[-1]['mu_star']}")
-    if np.abs(dflog.iloc[-1]["New mu"] - dflog.iloc[-1]["mu_star"]) < np.abs(
-        dflog.iloc[0]["New mu"] - dflog.iloc[0]["mu_star"]
-    ):
-        print("mu is improving")
-    else:
-        print("mu is not improving")
-
-    print(f"sigma diff at start {dflog.iloc[0]['New sigma'] - dflog.iloc[0]['mu_std']}")
-    print(f"sigma diff at end {dflog.iloc[-1]['New sigma'] - dflog.iloc[-1]['mu_std']}")
-    if np.abs(dflog.iloc[-1]["New sigma"] - dflog.iloc[-1]["mu_std"]) < np.abs(
-        dflog.iloc[0]["New sigma"] - dflog.iloc[0]["mu_std"]
-    ):
-        print("sigma is improving")
-    else:
-        print("sigma is not improving")
-
-    return dflog
-
-
-# _ = debug_SGD_1(w1p, data)
-# _ = debug_SGD_1(w1n, data)
-# _ = debug_SGD_1(w2p, data)
-# _ = debug_SGD_1(w2n, data)
-
-
-# TODO EXPLAIN WHY += WORKS HERE.
-
-
-## Problem 2
-x = np.array([8, 16, 22, 33, 50, 51])
-y = np.array([5, 20, 14, 32, 42, 58])
-
-# $-\frac{n}{\sigma}+\frac{1}{\sigma^3}\sum_{i=1}^n(y_i - (mx+c))^2$
-dsigma = lambda sigma, c, m, x: -len(x) / sigma + np.sum(
-    [(xi - (m * x + c)) ** 2 for xi in x]
-) / (sigma**3)
-# $-\frac{1}{\sigma^2}\sum_{i=1}^n(y_i - (mx+c))$
-dc = lambda sigma, c, m, x: -np.sum([xi - (m * x + c) for xi in x]) / (sigma**2)
-# $-\frac{1}{\sigma^2}\sum_{i=1}^n(x_i(y_i - (mx+c)))$
-dm = lambda sigma, c, m, x: -np.sum([x * (xi - (m * x + c)) for xi in x]) / (sigma**2)
-
-
-log2 = []
-
-
-def SGD_problem2(
-    sigma: float,
-    c: float,
-    m: float,
-    x: np.array,
-    y: np.array,
-    learning_rate=0.01,
-    n_epochs=1000,
-):
-    global log2
-    log2 = []
-    for epoch in range(n_epochs):
-        sigma += learning_rate * dsigma(sigma, c, m, x)
-        c += learning_rate * dc(sigma, c, m, x)
-        m += learning_rate * dm(sigma, c, m, x)
-
-        log2.append(
-            {
-                "Epoch": epoch,
-                "New sigma": sigma,
-                "New c": c,
-                "New m": m,
-                "dc": dc(sigma, c, m, x),
-                "dm": dm(sigma, c, m, x),
-                "dsigma": dsigma(sigma, c, m, x),
-                "Loss": loss((m * x + c), sigma, y),
-            }
-        )
-        print(f"Epoch {epoch}, Loss: {loss((m * x + c), sigma, y)}")
-    return np.array([sigma, c, m])
-
-
-# def debug_SGD_2(wnn, data):
-#     print("SGD Problem 2")
-#     print("wnn", SGD_problem2(*wnn, data))
-#     dflog = pd.DataFrame(log)
-#     dflog["m_star"] = m_star
-#     dflog["c_star"] = c_star
-#     dflog["sigma_star"] = sigma_star
-#     print(f"m diff at start {dflog.iloc[0]['New m'] - dflog.iloc[0]['m_star']}")
-#     print(f"m diff at end {dflog.iloc[-1]['New m'] - dflog.iloc[-1]['m_star']}")
-#     if np.abs(dflog.iloc[-1]["New m"] - dflog.iloc[-1]["m_star"]) < np.abs(
-#         dflog.iloc[0]["New m"] - dflog.iloc[0]["m_star"]
-#     ):
-#         print("m is improving")
-#     else:
-#         print("m is not improving")
-#     print(f"c diff at start {dflog.iloc[0]['New c'] - dflog.iloc[0]['c_star']}")
-#     print(f"c diff at end {dflog.iloc[-1]['New c'] - dflog.iloc[-1]['c_star']}")
-#     if np.abs(dflog.iloc[-1]["New c"] - dflog.iloc[-1]["c_star"]) < np.abs(
-#         dflog.iloc[0]["New c"] - dflog.iloc[0]["c_star"]
-#     ):
-#         print("c is improving")
-#     else:
-#         print("c is not improving")
-#     print(f"sigma diff at start {dflog.iloc[0]['New sigma'] - dflog.iloc[0]['sigma_star']}")
-#     print(f"sigma diff at end {dflog.iloc[-1]['New sigma'] - dflog.iloc[-1]['sigma_star']}")
-#     if np.abs(dflog.iloc[-1]["New sigma"] - dflog.iloc[-1]["sigma_star"]) < np.abs(
-#         dflog.iloc[0]["New sigma"] - dflog.iloc[0]["sigma_star"]
-#     ):
-#         print("sigma is improving")
-#     else:
-#         print("sigma is not improving")
-#     return dflog
-
-result = SGD_problem2(0.5, 0.5, 0.5, x, y)
-print(f"final parameters: m={result[2]}, c={result[1]}, sigma={result[0]}")
-
-
-## pset2
-# Knowing that the poisson pdf is $P(k) = \frac{\lambda^k e^{-\lambda}}{k!}$, we can find the negative log likelihood of the data as $-\log(\Pi_{i=1}^n P(k_i)) = -\sum_{i=1}^n \log(\frac{\lambda^k_i e^{-\lambda}}{k_i!}) = \sum_{i=1}^n -\ln(\lambda) k_i + \ln(k_i!) + \lambda$. Which simplified, gives $n\lambda + \sum_{i=1}^n \ln(k_i!) - \sum_{i=1}^n k_i \ln(\lambda)$. Differentiating with respect to $\lambda$ gives $n - \sum_{i=1}^n \frac{k_i}{\lambda}$. Which is our desired $\frac{\partial L}{\partial \lambda}$!
-
-
-import pandas as pd
-
-df = pd.read_csv("../data/01_raw/nyc_bb_bicyclist_counts.csv")
-
-dlambda = lambda l, k: len(k) - np.sum([ki / l for ki in k])
-
-
-def SGD_problem3(
-    l: float,
-    k: np.array,
-    learning_rate=0.01,
-    n_epochs=1000,
-):
-    global log3
-    log3 = []
-    for epoch in range(n_epochs):
-        l -= learning_rate * dlambda(l, k)
-        # $n\lambda + \sum_{i=1}^n \ln(k_i!) - \sum_{i=1}^n k_i \ln(\lambda)$
-        # the rest of the loss function is commented out because it's a
-        # constant and was causing overflows. It is unnecessary, and a useless
-        # pain.
-        loss = len(k) * l - np.sum(
-            [ki * np.log(l) for ki in k]
-        )  # + np.sum([np.log(np.math.factorial(ki)) for ki in k])
-
-        log3.append(
-            {
-                "Epoch": epoch,
-                "New lambda": l,
-                "dlambda": dlambda(l, k),
-                "Loss": loss,
-            }
-        )
-        print(f"Epoch {epoch}", f"Loss: {loss}")
-    return np.array([l])
-
-
-l_star = df["BB_COUNT"].mean()
-
-
-def debug_SGD_3(data, l=1000):
-    print("SGD Problem 3")
-    print(f"l: {SGD_problem3(l, data)}")
-    dflog = pd.DataFrame(log3)
-    dflog["l_star"] = l_star
-    print(f"l diff at start {dflog.iloc[0]['New lambda'] - dflog.iloc[0]['l_star']}")
-    print(f"l diff at end {dflog.iloc[-1]['New lambda'] - dflog.iloc[-1]['l_star']}")
-    if np.abs(dflog.iloc[-1]["New lambda"] - dflog.iloc[-1]["l_star"]) < np.abs(
-        dflog.iloc[0]["New lambda"] - dflog.iloc[0]["l_star"]
-    ):
-        print("l is improving")
-    else:
-        print("l is not improving")
-    return dflog
-
-
-debug_SGD_3(data=df["BB_COUNT"].values, l=l_star + 1000)
-debug_SGD_3(data=df["BB_COUNT"].values, l=l_star - 1000)
-
-
-## pset 4
-
-# dw = lambda w, x: len(x) * np.exp(np.dot(x, w)) * x - np.sum()
-
-primitive = lambda xi, wi: (x.shape[0] * np.exp(wi * xi) * xi) - (xi**2)
-p_dw = lambda w, xi: np.array([primitive(xi, wi) for xi, wi in ])
-
-
-def SGD_problem4(
-    w: np.array,
-    x: np.array,
-    learning_rate=0.01,
-    n_epochs=1000,
-):
-    global log4
-    log4 = []
-    for epoch in range(n_epochs):
-        w -= learning_rate * p_dw(w, x)
-        # custom
-        # loss = x.shape[0] * np.exp(np.dot(x, w))
-        loss_fn = lambda k, l: len(k) * l - np.sum(
-            [ki * np.log(l) for ki in k]
-        )  # + np.sum([np.log(np.math.factorial(ki)) for ki in k])
-        loss = loss_fn(x, np.exp(np.dot(x, w)))
-        log4.append(
-            {
-                "Epoch": epoch,
-                "New w": w,
-                "dw": dw(w, x),
-                "Loss": loss,
-            }
-        )
-        print(f"Epoch {epoch}", f"Loss: {loss}")
-    return w
-
-
-def debug_SGD_3(data, w=np.array([1, 1])):
-    print("SGD Problem 4")
-    print(f"w: {SGD_problem4(w, data)}")
-    dflog = pd.DataFrame(log4)
-    return dflog
-
-
-_ = debug_SGD_3(
-    data=df[["HIGH_T", "LOW_T", "PRECIP"]].to_numpy(),
-    w=np.array([1.0, 1.0, 1.0]),
-)

assignment-2/assignment_2/Gaussian Maximum Likelihood.ipynb

@@ -1,1676 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "fce8933f-4594-4bb5-bffe-86fcb9ddd684",
-   "metadata": {},
-   "source": [
-    "# MLE of a Gaussian $p_{model}(x|w)$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "f6cd23f0-e755-48af-be5e-aaee83dda1e7",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "data = [4, 5, 7, 8, 8, 9, 10, 5, 2, 3, 5, 4, 8, 9]\n",
-    "\n",
-    "\n",
-    "## imports\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "from scipy.optimize import minimize\n",
-    "from scipy.stats import norm\n",
-    "import math\n",
-    "\n",
-    "\n",
-    "## Problem 1\n",
-    "data = [4, 5, 7, 8, 8, 9, 10, 5, 2, 3, 5, 4, 8, 9]\n",
-    "\n",
-    "data_mean = np.mean(data)\n",
-    "data_variance = np.var(data)\n",
-    "\n",
-    "\n",
-    "mu = 0.5\n",
-    "sigma = 0.5\n",
-    "w = np.array([mu, sigma])\n",
-    "\n",
-    "w_star = np.array([data_mean, data_variance])\n",
-    "mu_star = data_mean\n",
-    "sigma_star = np.sqrt(data_variance)\n",
-    "offset = 10 * np.random.random(2)\n",
-    "\n",
-    "w1p = w_star + 0.5 * offset\n",
-    "w1n = w_star - 0.5 * offset\n",
-    "w2p = w_star + 0.25 * offset\n",
-    "w2n = w_star - 0.25 * offset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f3d8587b-3862-4e98-bbcc-99d57bb313c1",
-   "metadata": {},
-   "source": [
-    "Negative Log Likelihood is defined as follows: $-\\ln(\\frac{1}{\\sqrt{2\\pi\\sigma^2}}\\exp(-\\frac{1}{2}\\frac{(x-\\mu)}{\\sigma}^2))$. Ignoring the contribution of the constant, we find that $\\frac{\\delta}{\\delta \\mu} \\mathcal{N} = \\frac{\\mu-x}{\\sigma^2}$ and $\\frac{\\delta}{\\delta \\sigma} \\mathcal{N} = \\frac{\\sigma^2 + (\\mu-x)^2 - \\sigma^2}{\\sigma^3}$. We apply these as our step functions for our SGD. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "27bf27ad-031e-4b65-a44d-53c5c1a09d91",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "loss = lambda mu, sigma, x: np.sum(\n",
-    "    [-np.log(norm.pdf(xi, loc=mu, scale=sigma)) for xi in x]\n",
-    ")\n",
-    "\n",
-    "loss_2_alternative = lambda mu, sigma, x: -len(x) / 2 * np.log(\n",
-    "    2 * np.pi * sigma**2\n",
-    ") - 1 / (2 * sigma**2) * np.sum((x - mu) ** 2)\n",
-    "\n",
-    "\n",
-    "dmu = lambda mu, sigma, x: -np.sum([mu - xi for xi in x]) / (sigma**2)\n",
-    "dsigma = lambda mu, sigma, x: -len(x) / sigma + np.sum([(mu - xi) ** 2 for xi in x]) / (sigma**3)\n",
-    "\n",
-    "log = []\n",
-    "def SGD_problem1(mu, sigma, x, learning_rate=0.01, n_epochs=1000):\n",
-    "    global log\n",
-    "    log = []\n",
-    "    for epoch in range(n_epochs):\n",
-    "        mu += learning_rate * dmu(mu, sigma, x)\n",
-    "        sigma += learning_rate * dsigma(mu, sigma, x)\n",
-    "\n",
-    "        # print(f\"Epoch {epoch}, Loss: {loss(mu, sigma, x)}, New mu: {mu}, New sigma: {sigma}\")\n",
-    "        log.append(\n",
-    "            {\n",
-    "                \"Epoch\": epoch,\n",
-    "                \"Loss\": loss(mu, sigma, x),\n",
-    "                \"Loss 2 Alternative\": loss_2_alternative(mu, sigma, x),\n",
-    "                \"New mu\": mu,\n",
-    "                \"New sigma\": sigma,\n",
-    "            }\n",
-    "        )\n",
-    "    return np.array([mu, sigma])\n",
-    "\n",
-    "\n",
-    "def debug_SGD_1(wnn, data):\n",
-    "    print(\"SGD Problem 1\")\n",
-    "    print(\"wnn\", SGD_problem1(*wnn, data))\n",
-    "    dflog = pd.DataFrame(log)\n",
-    "    dflog[\"mu_star\"] = mu_star\n",
-    "    dflog[\"mu_std\"] = sigma_star\n",
-    "    print(f\"mu diff at start {dflog.iloc[0]['New mu'] - dflog.iloc[0]['mu_star']}\")\n",
-    "    print(f\"mu diff at end {dflog.iloc[-1]['New mu'] - dflog.iloc[-1]['mu_star']}\")\n",
-    "    if np.abs(dflog.iloc[-1][\"New mu\"] - dflog.iloc[-1][\"mu_star\"]) < np.abs(\n",
-    "        dflog.iloc[0][\"New mu\"] - dflog.iloc[0][\"mu_star\"]\n",
-    "    ):\n",
-    "        print(\"mu is improving\")\n",
-    "    else:\n",
-    "        print(\"mu is not improving\")\n",
-    "\n",
-    "    print(f\"sigma diff at start {dflog.iloc[0]['New sigma'] - dflog.iloc[0]['mu_std']}\")\n",
-    "    print(f\"sigma diff at end {dflog.iloc[-1]['New sigma'] - dflog.iloc[-1]['mu_std']}\")\n",
-    "    if np.abs(dflog.iloc[-1][\"New sigma\"] - dflog.iloc[-1][\"mu_std\"]) < np.abs(\n",
-    "        dflog.iloc[0][\"New sigma\"] - dflog.iloc[0][\"mu_std\"]\n",
-    "    ):\n",
-    "        print(\"sigma is improving\")\n",
-    "    else:\n",
-    "        print(\"sigma is not improving\")\n",
-    "\n",
-    "    return dflog"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "27dd3bc6-b96e-4f8b-9118-01ad344dfd6a",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "SGD Problem 1\n",
-      "wnn [6.2142858  2.42541812]\n",
-      "mu diff at start 0.27610721776969527\n",
-      "mu diff at end 8.978893806244059e-08\n",
-      "mu is improving\n",
-      "sigma diff at start 8.134860851821205\n",
-      "sigma diff at end 1.7124079931818414e-12\n",
-      "sigma is improving\n",
-      "SGD Problem 1\n",
-      "wnn [6.21428571 2.42541812]\n",
-      "mu diff at start -0.24923650064862635\n",
-      "mu diff at end -6.602718372050731e-12\n",
-      "mu is improving\n",
-      "sigma diff at start -0.859536014291925\n",
-      "sigma diff at end -3.552713678800501e-15\n",
-      "sigma is improving\n",
-      "SGD Problem 1\n",
-      "wnn [6.21428572 2.42541812]\n",
-      "mu diff at start 0.13794086144778994\n",
-      "mu diff at end 1.0008935902305893e-09\n",
-      "mu is improving\n",
-      "sigma diff at start 5.786783512688555\n",
-      "sigma diff at end 4.440892098500626e-15\n",
-      "sigma is improving\n",
-      "SGD Problem 1\n",
-      "wnn [6.21428571 2.42541812]\n",
-      "mu diff at start -0.13668036978891251\n",
-      "mu diff at end -8.528289185960602e-12\n",
-      "mu is improving\n",
-      "sigma diff at start 1.091241177336173\n",
-      "sigma diff at end 4.440892098500626e-15\n",
-      "sigma is improving\n"
-     ]
-    }
-   ],
-   "source": [
-    "_ = debug_SGD_1(w1p, data)\n",
-    "_ = debug_SGD_1(w1n, data)\n",
-    "_ = debug_SGD_1(w2p, data)\n",
-    "_ = debug_SGD_1(w2n, data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "30096401-0bd5-4cf6-b093-a688476e16f1",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "# MLE of Conditional Gaussian"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "101a3c5e-1e02-41e6-9eab-aba65c39627a",
-   "metadata": {},
-   "source": [
-    "dsigma = $-\\frac{n}{\\sigma}+\\frac{1}{\\sigma^3}\\sum_{i=1}^n(y_i - (mx+c))^2$  \n",
-    "dc = $-\\frac{1}{\\sigma^2}\\sum_{i=1}^n(y_i - (mx+c))$  \n",
-    "dm = $-\\frac{1}{\\sigma^2}\\sum_{i=1}^n(x_i(y_i - (mx+c)))$  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "21969012-f81b-43d4-975d-13411e975f8f",
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 0, Loss: 297.82677563555086\n",
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-      "Epoch 726, Loss: 297.8262754770806\n",
-      "Epoch 727, Loss: 297.826274815622\n",
-      "Epoch 728, Loss: 297.82627415424554\n",
-      "Epoch 729, Loss: 297.82627349295143\n",
-      "Epoch 730, Loss: 297.8262728317395\n",
-      "Epoch 731, Loss: 297.82627217061\n",
-      "Epoch 732, Loss: 297.82627150956284\n",
-      "Epoch 733, Loss: 297.8262708485981\n",
-      "Epoch 734, Loss: 297.82627018771575\n",
-      "Epoch 735, Loss: 297.8262695269158\n",
-      "Epoch 736, Loss: 297.8262688661983\n",
-      "Epoch 737, Loss: 297.82626820556334\n",
-      "Epoch 738, Loss: 297.826267545011\n",
-      "Epoch 739, Loss: 297.8262668845411\n",
-      "Epoch 740, Loss: 297.82626622415387\n",
-      "Epoch 741, Loss: 297.82626556384935\n",
-      "Epoch 742, Loss: 297.82626490362736\n",
-      "Epoch 743, Loss: 297.826264243488\n",
-      "Epoch 744, Loss: 297.82626358343146\n",
-      "Epoch 745, Loss: 297.82626292345765\n",
-      "Epoch 746, Loss: 297.82626226356655\n",
-      "Epoch 747, Loss: 297.8262616037582\n",
-      "Epoch 748, Loss: 297.8262609440329\n",
-      "Epoch 749, Loss: 297.8262602843903\n",
-      "Epoch 750, Loss: 297.82625962483047\n",
-      "Epoch 751, Loss: 297.82625896535376\n",
-      "Epoch 752, Loss: 297.82625830595987\n",
-      "Epoch 753, Loss: 297.8262576466491\n",
-      "Epoch 754, Loss: 297.82625698742123\n",
-      "Epoch 755, Loss: 297.82625632827643\n",
-      "Epoch 756, Loss: 297.8262556692147\n",
-      "Epoch 757, Loss: 297.82625501023597\n",
-      "Epoch 758, Loss: 297.8262543513405\n",
-      "Epoch 759, Loss: 297.8262536925281\n",
-      "Epoch 760, Loss: 297.82625303379893\n",
-      "Epoch 761, Loss: 297.8262523751529\n",
-      "Epoch 762, Loss: 297.82625171659015\n",
-      "Epoch 763, Loss: 297.82625105811076\n",
-      "Epoch 764, Loss: 297.8262503997146\n",
-      "Epoch 765, Loss: 297.82624974140174\n",
-      "Epoch 766, Loss: 297.82624908317234\n",
-      "Epoch 767, Loss: 297.8262484250262\n",
-      "Epoch 768, Loss: 297.82624776696355\n",
-      "Epoch 769, Loss: 297.8262471089844\n",
-      "Epoch 770, Loss: 297.82624645108865\n",
-      "Epoch 771, Loss: 297.8262457932765\n",
-      "Epoch 772, Loss: 297.82624513554777\n",
-      "Epoch 773, Loss: 297.8262444779027\n",
-      "Epoch 774, Loss: 297.8262438203412\n",
-      "Epoch 775, Loss: 297.8262431628633\n",
-      "Epoch 776, Loss: 297.8262425054691\n",
-      "Epoch 777, Loss: 297.82624184815865\n",
-      "Epoch 778, Loss: 297.82624119093174\n",
-      "Epoch 779, Loss: 297.82624053378873\n",
-      "Epoch 780, Loss: 297.82623987672946\n",
-      "Epoch 781, Loss: 297.826239219754\n",
-      "Epoch 782, Loss: 297.8262385628624\n",
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-      "Epoch 784, Loss: 297.82623724933075\n",
-      "Epoch 785, Loss: 297.82623659269086\n",
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-      "Epoch 787, Loss: 297.8262352796629\n",
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-      "Epoch 789, Loss: 297.8262339669709\n",
-      "Epoch 790, Loss: 297.8262333107511\n",
-      "Epoch 791, Loss: 297.82623265461535\n",
-      "Epoch 792, Loss: 297.8262319985638\n",
-      "Epoch 793, Loss: 297.8262313425964\n",
-      "Epoch 794, Loss: 297.8262306867131\n",
-      "Epoch 795, Loss: 297.8262300309141\n",
-      "Epoch 796, Loss: 297.82622937519943\n",
-      "Epoch 797, Loss: 297.826228719569\n",
-      "Epoch 798, Loss: 297.82622806402287\n",
-      "Epoch 799, Loss: 297.82622740856107\n",
-      "Epoch 800, Loss: 297.8262267531837\n",
-      "Epoch 801, Loss: 297.82622609789064\n",
-      "Epoch 802, Loss: 297.826225442682\n",
-      "Epoch 803, Loss: 297.826224787558\n",
-      "Epoch 804, Loss: 297.8262241325183\n",
-      "Epoch 805, Loss: 297.8262234775633\n",
-      "Epoch 806, Loss: 297.8262228226928\n",
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-      "Epoch 809, Loss: 297.8262208585888\n",
-      "Epoch 810, Loss: 297.8262202040569\n",
-      "Epoch 811, Loss: 297.8262195496096\n",
-      "Epoch 812, Loss: 297.82621889524705\n",
-      "Epoch 813, Loss: 297.82621824096935\n",
-      "Epoch 814, Loss: 297.8262175867764\n",
-      "Epoch 815, Loss: 297.8262169326682\n",
-      "Epoch 816, Loss: 297.82621627864495\n",
-      "Epoch 817, Loss: 297.82621562470666\n",
-      "Epoch 818, Loss: 297.8262149708532\n",
-      "Epoch 819, Loss: 297.82621431708463\n",
-      "Epoch 820, Loss: 297.8262136634011\n",
-      "Epoch 821, Loss: 297.82621300980264\n",
-      "Epoch 822, Loss: 297.82621235628915\n",
-      "Epoch 823, Loss: 297.82621170286075\n",
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-      "Epoch 826, Loss: 297.8262097430863\n",
-      "Epoch 827, Loss: 297.8262090899985\n",
-      "Epoch 828, Loss: 297.82620843699596\n",
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-      "Epoch 832, Loss: 297.8262058258384\n",
-      "Epoch 833, Loss: 297.82620517326245\n",
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-      "Epoch 835, Loss: 297.82620386836675\n",
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-      "Epoch 837, Loss: 297.8262025638128\n",
-      "Epoch 838, Loss: 297.82620191166416\n",
-      "Epoch 839, Loss: 297.8262012596011\n",
-      "Epoch 840, Loss: 297.82620060762355\n",
-      "Epoch 841, Loss: 297.8261999557316\n",
-      "Epoch 842, Loss: 297.8261993039254\n",
-      "Epoch 843, Loss: 297.8261986522048\n",
-      "Epoch 844, Loss: 297.82619800056995\n",
-      "Epoch 845, Loss: 297.8261973490208\n",
-      "Epoch 846, Loss: 297.82619669755746\n",
-      "Epoch 847, Loss: 297.8261960461799\n",
-      "Epoch 848, Loss: 297.82619539488826\n",
-      "Epoch 849, Loss: 297.82619474368244\n",
-      "Epoch 850, Loss: 297.82619409256245\n",
-      "Epoch 851, Loss: 297.8261934415284\n",
-      "Epoch 852, Loss: 297.82619279058036\n",
-      "Epoch 853, Loss: 297.82619213971833\n",
-      "Epoch 854, Loss: 297.8261914889422\n",
-      "Epoch 855, Loss: 297.82619083825216\n",
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-      "Epoch 858, Loss: 297.82618888669873\n",
-      "Epoch 859, Loss: 297.8261882363531\n",
-      "Epoch 860, Loss: 297.8261875860939\n",
-      "Epoch 861, Loss: 297.8261869359208\n",
-      "Epoch 862, Loss: 297.826186285834\n",
-      "Epoch 863, Loss: 297.82618563583344\n",
-      "Epoch 864, Loss: 297.8261849859192\n",
-      "Epoch 865, Loss: 297.8261843360914\n",
-      "Epoch 866, Loss: 297.82618368634996\n",
-      "Epoch 867, Loss: 297.8261830366949\n",
-      "Epoch 868, Loss: 297.82618238712627\n",
-      "Epoch 869, Loss: 297.82618173764416\n",
-      "Epoch 870, Loss: 297.8261810882486\n",
-      "Epoch 871, Loss: 297.82618043893956\n",
-      "Epoch 872, Loss: 297.826179789717\n",
-      "Epoch 873, Loss: 297.8261791405811\n",
-      "Epoch 874, Loss: 297.82617849153183\n",
-      "Epoch 875, Loss: 297.82617784256917\n",
-      "Epoch 876, Loss: 297.8261771936933\n",
-      "Epoch 877, Loss: 297.8261765449042\n",
-      "Epoch 878, Loss: 297.8261758962016\n",
-      "Epoch 879, Loss: 297.826175247586\n",
-      "Epoch 880, Loss: 297.82617459905725\n",
-      "Epoch 881, Loss: 297.82617395061516\n",
-      "Epoch 882, Loss: 297.8261733022601\n",
-      "Epoch 883, Loss: 297.82617265399193\n",
-      "Epoch 884, Loss: 297.82617200581075\n",
-      "Epoch 885, Loss: 297.8261713577164\n",
-      "Epoch 886, Loss: 297.8261707097091\n",
-      "Epoch 887, Loss: 297.82617006178884\n",
-      "Epoch 888, Loss: 297.8261694139557\n",
-      "Epoch 889, Loss: 297.82616876620966\n",
-      "Epoch 890, Loss: 297.82616811855064\n",
-      "Epoch 891, Loss: 297.8261674709788\n",
-      "Epoch 892, Loss: 297.82616682349425\n",
-      "Epoch 893, Loss: 297.8261661760969\n",
-      "Epoch 894, Loss: 297.82616552878676\n",
-      "Epoch 895, Loss: 297.82616488156384\n",
-      "Epoch 896, Loss: 297.8261642344284\n",
-      "Epoch 897, Loss: 297.8261635873801\n",
-      "Epoch 898, Loss: 297.82616294041935\n",
-      "Epoch 899, Loss: 297.8261622935459\n",
-      "Epoch 900, Loss: 297.82616164676\n",
-      "Epoch 901, Loss: 297.8261610000614\n",
-      "Epoch 902, Loss: 297.8261603534504\n",
-      "Epoch 903, Loss: 297.82615970692694\n",
-      "Epoch 904, Loss: 297.826159060491\n",
-      "Epoch 905, Loss: 297.82615841414264\n",
-      "Epoch 906, Loss: 297.82615776788197\n",
-      "Epoch 907, Loss: 297.826157121709\n",
-      "Epoch 908, Loss: 297.82615647562363\n",
-      "Epoch 909, Loss: 297.8261558296259\n",
-      "Epoch 910, Loss: 297.8261551837161\n",
-      "Epoch 911, Loss: 297.826154537894\n",
-      "Epoch 912, Loss: 297.82615389215977\n",
-      "Epoch 913, Loss: 297.82615324651323\n",
-      "Epoch 914, Loss: 297.8261526009547\n",
-      "Epoch 915, Loss: 297.826151955484\n",
-      "Epoch 916, Loss: 297.8261513101013\n",
-      "Epoch 917, Loss: 297.8261506648065\n",
-      "Epoch 918, Loss: 297.8261500195997\n",
-      "Epoch 919, Loss: 297.826149374481\n",
-      "Epoch 920, Loss: 297.82614872945044\n",
-      "Epoch 921, Loss: 297.8261480845078\n",
-      "Epoch 922, Loss: 297.8261474396533\n",
-      "Epoch 923, Loss: 297.8261467948871\n",
-      "Epoch 924, Loss: 297.826146150209\n",
-      "Epoch 925, Loss: 297.82614550561914\n",
-      "Epoch 926, Loss: 297.8261448611175\n",
-      "Epoch 927, Loss: 297.82614421670417\n",
-      "Epoch 928, Loss: 297.8261435723791\n",
-      "Epoch 929, Loss: 297.8261429281424\n",
-      "Epoch 930, Loss: 297.8261422839941\n",
-      "Epoch 931, Loss: 297.82614163993424\n",
-      "Epoch 932, Loss: 297.8261409959628\n",
-      "Epoch 933, Loss: 297.82614035207973\n",
-      "Epoch 934, Loss: 297.82613970828527\n",
-      "Epoch 935, Loss: 297.8261390645793\n",
-      "Epoch 936, Loss: 297.826138420962\n",
-      "Epoch 937, Loss: 297.8261377774331\n",
-      "Epoch 938, Loss: 297.82613713399303\n",
-      "Epoch 939, Loss: 297.82613649064143\n",
-      "Epoch 940, Loss: 297.8261358473787\n",
-      "Epoch 941, Loss: 297.8261352042046\n",
-      "Epoch 942, Loss: 297.8261345611193\n",
-      "Epoch 943, Loss: 297.8261339181227\n",
-      "Epoch 944, Loss: 297.826133275215\n",
-      "Epoch 945, Loss: 297.82613263239614\n",
-      "Epoch 946, Loss: 297.8261319896662\n",
-      "Epoch 947, Loss: 297.82613134702507\n",
-      "Epoch 948, Loss: 297.826130704473\n",
-      "Epoch 949, Loss: 297.8261300620098\n",
-      "Epoch 950, Loss: 297.8261294196356\n",
-      "Epoch 951, Loss: 297.8261287773505\n",
-      "Epoch 952, Loss: 297.8261281351545\n",
-      "Epoch 953, Loss: 297.8261274930475\n",
-      "Epoch 954, Loss: 297.82612685102976\n",
-      "Epoch 955, Loss: 297.8261262091011\n",
-      "Epoch 956, Loss: 297.82612556726167\n",
-      "Epoch 957, Loss: 297.8261249255115\n",
-      "Epoch 958, Loss: 297.8261242838506\n",
-      "Epoch 959, Loss: 297.8261236422789\n",
-      "Epoch 960, Loss: 297.8261230007966\n",
-      "Epoch 961, Loss: 297.8261223594037\n",
-      "Epoch 962, Loss: 297.82612171810007\n",
-      "Epoch 963, Loss: 297.82612107688584\n",
-      "Epoch 964, Loss: 297.8261204357612\n",
-      "Epoch 965, Loss: 297.82611979472597\n",
-      "Epoch 966, Loss: 297.8261191537804\n",
-      "Epoch 967, Loss: 297.82611851292415\n",
-      "Epoch 968, Loss: 297.8261178721576\n",
-      "Epoch 969, Loss: 297.8261172314807\n",
-      "Epoch 970, Loss: 297.8261165908933\n",
-      "Epoch 971, Loss: 297.82611595039566\n",
-      "Epoch 972, Loss: 297.8261153099878\n",
-      "Epoch 973, Loss: 297.82611466966955\n",
-      "Epoch 974, Loss: 297.8261140294412\n",
-      "Epoch 975, Loss: 297.8261133893026\n",
-      "Epoch 976, Loss: 297.82611274925375\n",
-      "Epoch 977, Loss: 297.82611210929485\n",
-      "Epoch 978, Loss: 297.82611146942594\n",
-      "Epoch 979, Loss: 297.8261108296469\n",
-      "Epoch 980, Loss: 297.8261101899578\n",
-      "Epoch 981, Loss: 297.8261095503587\n",
-      "Epoch 982, Loss: 297.8261089108496\n",
-      "Epoch 983, Loss: 297.82610827143054\n",
-      "Epoch 984, Loss: 297.8261076321016\n",
-      "Epoch 985, Loss: 297.8261069928628\n",
-      "Epoch 986, Loss: 297.8261063537141\n",
-      "Epoch 987, Loss: 297.8261057146557\n",
-      "Epoch 988, Loss: 297.8261050756874\n",
-      "Epoch 989, Loss: 297.82610443680943\n",
-      "Epoch 990, Loss: 297.82610379802173\n",
-      "Epoch 991, Loss: 297.82610315932436\n",
-      "Epoch 992, Loss: 297.8261025207173\n",
-      "Epoch 993, Loss: 297.8261018822007\n",
-      "Epoch 994, Loss: 297.8261012437744\n",
-      "Epoch 995, Loss: 297.8261006054387\n",
-      "Epoch 996, Loss: 297.82609996719333\n",
-      "Epoch 997, Loss: 297.8260993290385\n",
-      "Epoch 998, Loss: 297.8260986909742\n",
-      "Epoch 999, Loss: 297.8260980530006\n",
-      "final parameters: m=0.45136980910052144, c=0.49775672565271384, sigma=1562.2616856027405\n"
-     ]
-    }
-   ],
-   "source": [
-    "## Problem 2\n",
-    "x = np.array([8, 16, 22, 33, 50, 51])\n",
-    "y = np.array([5, 20, 14, 32, 42, 58])\n",
-    "\n",
-    "# $-\\frac{n}{\\sigma}+\\frac{1}{\\sigma^3}\\sum_{i=1}^n(y_i - (mx+c))^2$\n",
-    "dsigma = lambda sigma, c, m, x: -len(x) / sigma + np.sum(\n",
-    "    [(xi - (m * x + c)) ** 2 for xi in x]\n",
-    ") / (sigma**3)\n",
-    "# $-\\frac{1}{\\sigma^2}\\sum_{i=1}^n(y_i - (mx+c))$\n",
-    "dc = lambda sigma, c, m, x: -np.sum([xi - (m * x + c) for xi in x]) / (sigma**2)\n",
-    "# $-\\frac{1}{\\sigma^2}\\sum_{i=1}^n(x_i(y_i - (mx+c)))$\n",
-    "dm = lambda sigma, c, m, x: -np.sum([x * (xi - (m * x + c)) for xi in x]) / (sigma**2)\n",
-    "\n",
-    "\n",
-    "log2 = []\n",
-    "\n",
-    "\n",
-    "def SGD_problem2(\n",
-    "    sigma: float,\n",
-    "    c: float,\n",
-    "    m: float,\n",
-    "    x: np.array,\n",
-    "    y: np.array,\n",
-    "    learning_rate=0.01,\n",
-    "    n_epochs=1000,\n",
-    "):\n",
-    "    global log2\n",
-    "    log2 = []\n",
-    "    for epoch in range(n_epochs):\n",
-    "        sigma += learning_rate * dsigma(sigma, c, m, x)\n",
-    "        c += learning_rate * dc(sigma, c, m, x)\n",
-    "        m += learning_rate * dm(sigma, c, m, x)\n",
-    "\n",
-    "        log2.append(\n",
-    "            {\n",
-    "                \"Epoch\": epoch,\n",
-    "                \"New sigma\": sigma,\n",
-    "                \"New c\": c,\n",
-    "                \"New m\": m,\n",
-    "                \"dc\": dc(sigma, c, m, x),\n",
-    "                \"dm\": dm(sigma, c, m, x),\n",
-    "                \"dsigma\": dsigma(sigma, c, m, x),\n",
-    "                \"Loss\": loss((m * x + c), sigma, y),\n",
-    "            }\n",
-    "        )\n",
-    "        print(f\"Epoch {epoch}, Loss: {loss((m * x + c), sigma, y)}\")\n",
-    "    return np.array([sigma, c, m])\n",
-    "\n",
-    "\n",
-    "result = SGD_problem2(0.5, 0.5, 0.5, x, y)\n",
-    "print(f\"final parameters: m={result[2]}, c={result[1]}, sigma={result[0]}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0562b012-f4ca-47de-bc76-e0eb2bf1e509",
-   "metadata": {},
-   "source": [
-    "loss appears to be decreasing. Uncollapse cell for output"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bed9f3ce-c15c-4f30-8906-26f3e51acf30",
-   "metadata": {},
-   "source": [
-    "# Bike Rides and the Poisson Model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "975e2ef5-f5d5-45a3-b635-8faef035906f",
-   "metadata": {},
-   "source": [
-    "Knowing that the poisson pdf is $P(k) = \\frac{\\lambda^k e^{-\\lambda}}{k!}$, we can find the negative log likelihood of the data as $-\\log(\\Pi_{i=1}^n P(k_i)) = -\\sum_{i=1}^n \\log(\\frac{\\lambda^k_i e^{-\\lambda}}{k_i!}) = \\sum_{i=1}^n -\\ln(\\lambda) k_i + \\ln(k_i!) + \\lambda$. Which simplified, gives $n\\lambda + \\sum_{i=1}^n \\ln(k_i!) - \\sum_{i=1}^n k_i \\ln(\\lambda)$. Differentiating with respect to $\\lambda$ gives $n - \\sum_{i=1}^n \\frac{k_i}{\\lambda}$. Which is our desired $\\frac{\\partial L}{\\partial \\lambda}$!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "3877723c-179e-4759-bed5-9eb70110ded2",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "SGD Problem 3\n",
-      "l: [3215.17703224]\n",
-      "l diff at start 999.4184849065878\n",
-      "l diff at end 535.134976163929\n",
-      "l is improving\n",
-      "SGD Problem 3\n",
-      "l: [2326.70336987]\n",
-      "l diff at start -998.7262223631474\n",
-      "l diff at end -353.33868620734074\n",
-      "l is improving\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>Epoch</th>\n",
-       "      <th>New lambda</th>\n",
-       "      <th>dlambda</th>\n",
-       "      <th>Loss</th>\n",
-       "      <th>l_star</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>1681.315834</td>\n",
-       "      <td>-127.119133</td>\n",
-       "      <td>-3.899989e+06</td>\n",
-       "      <td>2680.042056</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1682.587025</td>\n",
-       "      <td>-126.861418</td>\n",
-       "      <td>-3.900150e+06</td>\n",
-       "      <td>2680.042056</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>1683.855639</td>\n",
-       "      <td>-126.604614</td>\n",
-       "      <td>-3.900311e+06</td>\n",
-       "      <td>2680.042056</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1685.121685</td>\n",
-       "      <td>-126.348715</td>\n",
-       "      <td>-3.900471e+06</td>\n",
-       "      <td>2680.042056</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>1686.385173</td>\n",
-       "      <td>-126.093716</td>\n",
-       "      <td>-3.900631e+06</td>\n",
-       "      <td>2680.042056</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>995</th>\n",
-       "      <td>995</td>\n",
-       "      <td>2325.399976</td>\n",
-       "      <td>-32.636710</td>\n",
-       "      <td>-3.948159e+06</td>\n",
-       "      <td>2680.042056</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>996</th>\n",
-       "      <td>996</td>\n",
-       "      <td>2325.726343</td>\n",
-       "      <td>-32.602100</td>\n",
-       "      <td>-3.948170e+06</td>\n",
-       "      <td>2680.042056</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>997</th>\n",
-       "      <td>997</td>\n",
-       "      <td>2326.052364</td>\n",
-       "      <td>-32.567536</td>\n",
-       "      <td>-3.948180e+06</td>\n",
-       "      <td>2680.042056</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>998</th>\n",
-       "      <td>998</td>\n",
-       "      <td>2326.378040</td>\n",
-       "      <td>-32.533018</td>\n",
-       "      <td>-3.948191e+06</td>\n",
-       "      <td>2680.042056</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>999</th>\n",
-       "      <td>999</td>\n",
-       "      <td>2326.703370</td>\n",
-       "      <td>-32.498547</td>\n",
-       "      <td>-3.948201e+06</td>\n",
-       "      <td>2680.042056</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1000 rows × 5 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     Epoch   New lambda     dlambda          Loss       l_star\n",
-       "0        0  1681.315834 -127.119133 -3.899989e+06  2680.042056\n",
-       "1        1  1682.587025 -126.861418 -3.900150e+06  2680.042056\n",
-       "2        2  1683.855639 -126.604614 -3.900311e+06  2680.042056\n",
-       "3        3  1685.121685 -126.348715 -3.900471e+06  2680.042056\n",
-       "4        4  1686.385173 -126.093716 -3.900631e+06  2680.042056\n",
-       "..     ...          ...         ...           ...          ...\n",
-       "995    995  2325.399976  -32.636710 -3.948159e+06  2680.042056\n",
-       "996    996  2325.726343  -32.602100 -3.948170e+06  2680.042056\n",
-       "997    997  2326.052364  -32.567536 -3.948180e+06  2680.042056\n",
-       "998    998  2326.378040  -32.533018 -3.948191e+06  2680.042056\n",
-       "999    999  2326.703370  -32.498547 -3.948201e+06  2680.042056\n",
-       "\n",
-       "[1000 rows x 5 columns]"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "df = pd.read_csv(\"../data/01_raw/nyc_bb_bicyclist_counts.csv\")\n",
-    "\n",
-    "dlambda = lambda l, k: len(k) - np.sum([ki / l for ki in k])\n",
-    "\n",
-    "\n",
-    "def SGD_problem3(\n",
-    "    l: float,\n",
-    "    k: np.array,\n",
-    "    learning_rate=0.01,\n",
-    "    n_epochs=1000,\n",
-    "):\n",
-    "    global log3\n",
-    "    log3 = []\n",
-    "    for epoch in range(n_epochs):\n",
-    "        l -= learning_rate * dlambda(l, k)\n",
-    "        # $n\\lambda + \\sum_{i=1}^n \\ln(k_i!) - \\sum_{i=1}^n k_i \\ln(\\lambda)$\n",
-    "        # the rest of the loss function is commented out because it's a\n",
-    "        # constant and was causing overflows. It is unnecessary, and a useless\n",
-    "        # pain.\n",
-    "        loss = len(k) * l - np.sum(\n",
-    "            [ki * np.log(l) for ki in k]\n",
-    "        )  # + np.sum([np.log(np.math.factorial(ki)) for ki in k])\n",
-    "\n",
-    "        log3.append(\n",
-    "            {\n",
-    "                \"Epoch\": epoch,\n",
-    "                \"New lambda\": l,\n",
-    "                \"dlambda\": dlambda(l, k),\n",
-    "                \"Loss\": loss,\n",
-    "            }\n",
-    "        )\n",
-    "        # print(f\"Epoch {epoch}\", f\"Loss: {loss}\")\n",
-    "    return np.array([l])\n",
-    "\n",
-    "\n",
-    "l_star = df[\"BB_COUNT\"].mean()\n",
-    "\n",
-    "\n",
-    "def debug_SGD_3(data, l=1000):\n",
-    "    print(\"SGD Problem 3\")\n",
-    "    print(f\"l: {SGD_problem3(l, data)}\")\n",
-    "    dflog = pd.DataFrame(log3)\n",
-    "    dflog[\"l_star\"] = l_star\n",
-    "    print(f\"l diff at start {dflog.iloc[0]['New lambda'] - dflog.iloc[0]['l_star']}\")\n",
-    "    print(f\"l diff at end {dflog.iloc[-1]['New lambda'] - dflog.iloc[-1]['l_star']}\")\n",
-    "    if np.abs(dflog.iloc[-1][\"New lambda\"] - dflog.iloc[-1][\"l_star\"]) < np.abs(\n",
-    "        dflog.iloc[0][\"New lambda\"] - dflog.iloc[0][\"l_star\"]\n",
-    "    ):\n",
-    "        print(\"l is improving\")\n",
-    "    else:\n",
-    "        print(\"l is not improving\")\n",
-    "    return dflog\n",
-    "\n",
-    "\n",
-    "debug_SGD_3(data=df[\"BB_COUNT\"].values, l=l_star + 1000)\n",
-    "debug_SGD_3(data=df[\"BB_COUNT\"].values, l=l_star - 1000)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c05192f9-78ae-4bdb-9df5-cac91006d79f",
-   "metadata": {},
-   "source": [
-    "l approaches the l_star and decreases the loss function."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4955b868-7f67-4760-bf86-39f6edd55871",
-   "metadata": {},
-   "source": [
-    "## Maximum Likelihood II"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cd7e6e62-3f64-43e5-bf2c-3cb514411446",
-   "metadata": {},
-   "source": [
-    "The partial of the poisson was found to be $nE^{w.x}*x - \\sum_{i=1}^{n}k x.x$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "7c8b167d-c397-4155-93f3-d826c279fbb2",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "SGD Problem 4\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_615396/2481416868.py:22: RuntimeWarning: divide by zero encountered in log\n",
-      "  [ki * np.log(l) for ki in k]\n"
-     ]
-    },
-    {
-     "ename": "ValueError",
-     "evalue": "operands could not be broadcast together with shapes (3,) (214,) ",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[18], line 44\u001b[0m\n\u001b[1;32m     40\u001b[0m     dflog \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame(log4)\n\u001b[1;32m     41\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m dflog\n\u001b[0;32m---> 44\u001b[0m _ \u001b[38;5;241m=\u001b[39m \u001b[43mdebug_SGD_3\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     45\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mHIGH_T\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mLOW_T\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mPRECIP\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_numpy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     46\u001b[0m \u001b[43m    \u001b[49m\u001b[43mw\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1.0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1.0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1.0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     47\u001b[0m \u001b[43m)\u001b[49m\n",
-      "Cell \u001b[0;32mIn[18], line 39\u001b[0m, in \u001b[0;36mdebug_SGD_3\u001b[0;34m(data, w)\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdebug_SGD_3\u001b[39m(data, w\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39marray([\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m])):\n\u001b[1;32m     38\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSGD Problem 4\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 39\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mw: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[43mSGD_problem4\u001b[49m\u001b[43m(\u001b[49m\u001b[43mw\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;250;43m \u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     40\u001b[0m     dflog \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame(log4)\n\u001b[1;32m     41\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m dflog\n",
-      "Cell \u001b[0;32mIn[18], line 24\u001b[0m, in \u001b[0;36mSGD_problem4\u001b[0;34m(w, x, learning_rate, n_epochs)\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[38;5;66;03m# custom\u001b[39;00m\n\u001b[1;32m     20\u001b[0m \u001b[38;5;66;03m# loss = x.shape[0] * np.exp(np.dot(x, w))\u001b[39;00m\n\u001b[1;32m     21\u001b[0m loss_fn \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mlambda\u001b[39;00m k, l: \u001b[38;5;28mlen\u001b[39m(k) \u001b[38;5;241m*\u001b[39m l \u001b[38;5;241m-\u001b[39m np\u001b[38;5;241m.\u001b[39msum(\n\u001b[1;32m     22\u001b[0m     [ki \u001b[38;5;241m*\u001b[39m np\u001b[38;5;241m.\u001b[39mlog(l) \u001b[38;5;28;01mfor\u001b[39;00m ki \u001b[38;5;129;01min\u001b[39;00m k]\n\u001b[1;32m     23\u001b[0m )  \u001b[38;5;66;03m# + np.sum([np.log(np.math.factorial(ki)) for ki in k])\u001b[39;00m\n\u001b[0;32m---> 24\u001b[0m loss \u001b[38;5;241m=\u001b[39m \u001b[43mloss_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mw\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     25\u001b[0m log4\u001b[38;5;241m.\u001b[39mappend(\n\u001b[1;32m     26\u001b[0m     {\n\u001b[1;32m     27\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEpoch\u001b[39m\u001b[38;5;124m\"\u001b[39m: epoch,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     31\u001b[0m     }\n\u001b[1;32m     32\u001b[0m )\n\u001b[1;32m     33\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEpoch \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mepoch\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLoss: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mloss\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
-      "Cell \u001b[0;32mIn[18], line 22\u001b[0m, in \u001b[0;36mSGD_problem4.<locals>.<lambda>\u001b[0;34m(k, l)\u001b[0m\n\u001b[1;32m     18\u001b[0m w \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m=\u001b[39m learning_rate \u001b[38;5;241m*\u001b[39m dw(w, x)\n\u001b[1;32m     19\u001b[0m \u001b[38;5;66;03m# custom\u001b[39;00m\n\u001b[1;32m     20\u001b[0m \u001b[38;5;66;03m# loss = x.shape[0] * np.exp(np.dot(x, w))\u001b[39;00m\n\u001b[1;32m     21\u001b[0m loss_fn \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mlambda\u001b[39;00m k, l: \u001b[38;5;28mlen\u001b[39m(k) \u001b[38;5;241m*\u001b[39m l \u001b[38;5;241m-\u001b[39m np\u001b[38;5;241m.\u001b[39msum(\n\u001b[0;32m---> 22\u001b[0m     [ki \u001b[38;5;241m*\u001b[39m np\u001b[38;5;241m.\u001b[39mlog(l) \u001b[38;5;28;01mfor\u001b[39;00m ki \u001b[38;5;129;01min\u001b[39;00m k]\n\u001b[1;32m     23\u001b[0m )  \u001b[38;5;66;03m# + np.sum([np.log(np.math.factorial(ki)) for ki in k])\u001b[39;00m\n\u001b[1;32m     24\u001b[0m loss \u001b[38;5;241m=\u001b[39m loss_fn(x, np\u001b[38;5;241m.\u001b[39mexp(np\u001b[38;5;241m.\u001b[39mdot(x, w)))\n\u001b[1;32m     25\u001b[0m log4\u001b[38;5;241m.\u001b[39mappend(\n\u001b[1;32m     26\u001b[0m     {\n\u001b[1;32m     27\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEpoch\u001b[39m\u001b[38;5;124m\"\u001b[39m: epoch,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     31\u001b[0m     }\n\u001b[1;32m     32\u001b[0m )\n",
-      "Cell \u001b[0;32mIn[18], line 22\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     18\u001b[0m w \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m=\u001b[39m learning_rate \u001b[38;5;241m*\u001b[39m dw(w, x)\n\u001b[1;32m     19\u001b[0m \u001b[38;5;66;03m# custom\u001b[39;00m\n\u001b[1;32m     20\u001b[0m \u001b[38;5;66;03m# loss = x.shape[0] * np.exp(np.dot(x, w))\u001b[39;00m\n\u001b[1;32m     21\u001b[0m loss_fn \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mlambda\u001b[39;00m k, l: \u001b[38;5;28mlen\u001b[39m(k) \u001b[38;5;241m*\u001b[39m l \u001b[38;5;241m-\u001b[39m np\u001b[38;5;241m.\u001b[39msum(\n\u001b[0;32m---> 22\u001b[0m     [\u001b[43mki\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog\u001b[49m\u001b[43m(\u001b[49m\u001b[43ml\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m ki \u001b[38;5;129;01min\u001b[39;00m k]\n\u001b[1;32m     23\u001b[0m )  \u001b[38;5;66;03m# + np.sum([np.log(np.math.factorial(ki)) for ki in k])\u001b[39;00m\n\u001b[1;32m     24\u001b[0m loss \u001b[38;5;241m=\u001b[39m loss_fn(x, np\u001b[38;5;241m.\u001b[39mexp(np\u001b[38;5;241m.\u001b[39mdot(x, w)))\n\u001b[1;32m     25\u001b[0m log4\u001b[38;5;241m.\u001b[39mappend(\n\u001b[1;32m     26\u001b[0m     {\n\u001b[1;32m     27\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEpoch\u001b[39m\u001b[38;5;124m\"\u001b[39m: epoch,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     31\u001b[0m     }\n\u001b[1;32m     32\u001b[0m )\n",
-      "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (3,) (214,) "
-     ]
-    }
-   ],
-   "source": [
-    "## pset 4\n",
-    "\n",
-    "dw = lambda w, x: np.sum([len(x) * np.exp(np.dot(xi, w)) * x - np.sum(np.dot(x.T,x)) for xi in x])\n",
-    "\n",
-    "#primitive = lambda xi, wi: (x.shape[0] * np.exp(wi * xi) * xi) - (xi**2)\n",
-    "#p_dw = lambda w, xi: np.array([primitive(xi, wi) for xi, wi in ])\n",
-    "\n",
-    "\n",
-    "def SGD_problem4(\n",
-    "    w: np.array,\n",
-    "    x: np.array,\n",
-    "    learning_rate=0.01,\n",
-    "    n_epochs=1000,\n",
-    "):\n",
-    "    global log4\n",
-    "    log4 = []\n",
-    "    for epoch in range(n_epochs):\n",
-    "        w -= learning_rate * dw(w, x)\n",
-    "        # custom\n",
-    "        # loss = x.shape[0] * np.exp(np.dot(x, w))\n",
-    "        loss_fn = lambda k, l: len(k) * l - np.sum(\n",
-    "            [ki * np.log(l) for ki in k]\n",
-    "        )  # + np.sum([np.log(np.math.factorial(ki)) for ki in k])\n",
-    "        loss = loss_fn(x, np.exp(np.dot(x, w)))\n",
-    "        log4.append(\n",
-    "            {\n",
-    "                \"Epoch\": epoch,\n",
-    "                \"New w\": w,\n",
-    "                \"dw\": dw(w, x),\n",
-    "                \"Loss\": loss,\n",
-    "            }\n",
-    "        )\n",
-    "        print(f\"Epoch {epoch}\", f\"Loss: {loss}\")\n",
-    "    return w\n",
-    "\n",
-    "\n",
-    "def debug_SGD_3(data, w=np.array([1, 1])):\n",
-    "    print(\"SGD Problem 4\")\n",
-    "    print(f\"w: {SGD_problem4(w, data)}\")\n",
-    "    dflog = pd.DataFrame(log4)\n",
-    "    return dflog\n",
-    "\n",
-    "\n",
-    "_ = debug_SGD_3(\n",
-    "    data=df[[\"HIGH_T\", \"LOW_T\", \"PRECIP\"]].to_numpy(),\n",
-    "    w=np.array([1.0, 1.0, 1.0]),\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7c00197d-873d-41b0-a458-dc8478b40f52",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

assignment-2/data/01_raw/nyc_bb_bicyclist_counts.csv

@@ -1,215 +0,0 @@
-Date,HIGH_T,LOW_T,PRECIP,BB_COUNT
-1-Apr-17,46.00,37.00,0.00,606
-2-Apr-17,62.10,41.00,0.00,2021
-3-Apr-17,63.00,50.00,0.03,2470
-4-Apr-17,51.10,46.00,1.18,723
-5-Apr-17,63.00,46.00,0.00,2807
-6-Apr-17,48.90,41.00,0.73,461
-7-Apr-17,48.00,43.00,0.01,1222
-8-Apr-17,55.90,39.90,0.00,1674
-9-Apr-17,66.00,45.00,0.00,2375
-10-Apr-17,73.90,55.00,0.00,3324
-11-Apr-17,80.10,62.10,0.00,3887
-12-Apr-17,73.90,57.90,0.02,2565
-13-Apr-17,64.00,48.90,0.00,3353
-14-Apr-17,64.90,48.90,0.00,2942
-15-Apr-17,64.90,52.00,0.00,2253
-16-Apr-17,84.90,62.10,0.01,2877
-17-Apr-17,73.90,64.00,0.01,3152
-18-Apr-17,66.00,50.00,0.00,3415
-19-Apr-17,52.00,45.00,0.01,1965
-20-Apr-17,64.90,50.00,0.17,1567
-21-Apr-17,53.10,48.00,0.29,1426
-22-Apr-17,55.90,52.00,0.11,1318
-23-Apr-17,64.90,46.90,0.00,2520
-24-Apr-17,60.10,50.00,0.01,2544
-25-Apr-17,54.00,50.00,0.91,611
-26-Apr-17,59.00,54.00,0.34,1247
-27-Apr-17,68.00,59.00,0.00,2959
-28-Apr-17,82.90,57.90,0.00,3679
-29-Apr-17,84.00,64.00,0.06,3315
-30-Apr-17,64.00,54.00,0.00,2225
-1-May-17,72.00,50.00,0.00,3084
-2-May-17,73.90,66.90,0.00,3423
-3-May-17,64.90,57.90,0.00,3342
-4-May-17,63.00,50.00,0.00,3019
-5-May-17,59.00,52.00,3.02,513
-6-May-17,64.90,57.00,0.18,1892
-7-May-17,54.00,48.90,0.01,3539
-8-May-17,57.00,45.00,0.00,2886
-9-May-17,61.00,48.00,0.00,2718
-10-May-17,70.00,51.10,0.00,2810
-11-May-17,61.00,51.80,0.00,2657
-12-May-17,62.10,51.10,0.00,2640
-13-May-17,51.10,45.00,1.31,151
-14-May-17,64.90,46.00,0.02,1452
-15-May-17,66.90,55.90,0.00,2685
-16-May-17,78.10,57.90,0.00,3666
-17-May-17,90.00,66.00,0.00,3535
-18-May-17,91.90,75.00,0.00,3190
-19-May-17,90.00,75.90,0.00,2952
-20-May-17,64.00,55.90,0.01,2161
-21-May-17,66.90,55.00,0.00,2612
-22-May-17,61.00,54.00,0.59,768
-23-May-17,68.00,57.90,0.00,3174
-24-May-17,66.90,57.00,0.04,2969
-25-May-17,57.90,55.90,0.58,488
-26-May-17,73.00,55.90,0.10,2590
-27-May-17,71.10,61.00,0.00,2609
-28-May-17,71.10,59.00,0.00,2640
-29-May-17,57.90,55.90,0.13,836
-30-May-17,59.00,55.90,0.06,2301
-31-May-17,75.00,57.90,0.03,2689
-1-Jun-17,78.10,62.10,0.00,3468
-2-Jun-17,73.90,60.10,0.01,3271
-3-Jun-17,72.00,55.00,0.01,2589
-4-Jun-17,68.00,60.10,0.09,1805
-5-Jun-17,66.90,60.10,0.02,2171
-6-Jun-17,55.90,53.10,0.06,1193
-7-Jun-17,66.90,54.00,0.00,3211
-8-Jun-17,68.00,59.00,0.00,3253
-9-Jun-17,80.10,59.00,0.00,3401
-10-Jun-17,84.00,68.00,0.00,3066
-11-Jun-17,90.00,73.00,0.00,2465
-12-Jun-17,91.90,77.00,0.00,2854
-13-Jun-17,93.90,78.10,0.01,2882
-14-Jun-17,84.00,69.10,0.29,2596
-15-Jun-17,75.00,66.00,0.00,3510
-16-Jun-17,68.00,66.00,0.00,2054
-17-Jun-17,73.00,66.90,1.39,1399
-18-Jun-17,84.00,72.00,0.01,2199
-19-Jun-17,87.10,70.00,1.35,1648
-20-Jun-17,82.00,72.00,0.03,3407
-21-Jun-17,82.00,72.00,0.00,3304
-22-Jun-17,82.00,70.00,0.00,3368
-23-Jun-17,82.90,75.90,0.04,2283
-24-Jun-17,82.90,71.10,1.29,2307
-25-Jun-17,82.00,69.10,0.00,2625
-26-Jun-17,78.10,66.00,0.00,3386
-27-Jun-17,75.90,61.00,0.18,3182
-28-Jun-17,78.10,62.10,0.00,3766
-29-Jun-17,81.00,68.00,0.00,3356
-30-Jun-17,88.00,73.90,0.01,2687
-1-Jul-17,84.90,72.00,0.23,1848
-2-Jul-17,87.10,73.00,0.00,2467
-3-Jul-17,87.10,71.10,0.45,2714
-4-Jul-17,82.90,70.00,0.00,2296
-5-Jul-17,84.90,71.10,0.00,3170
-6-Jul-17,75.00,71.10,0.01,3065
-7-Jul-17,79.00,68.00,1.78,1513
-8-Jul-17,82.90,70.00,0.00,2718
-9-Jul-17,81.00,69.10,0.00,3048
-10-Jul-17,82.90,71.10,0.00,3506
-11-Jul-17,84.00,75.00,0.00,2929
-12-Jul-17,87.10,77.00,0.00,2860
-13-Jul-17,89.10,77.00,0.00,2563
-14-Jul-17,69.10,64.90,0.35,907
-15-Jul-17,82.90,68.00,0.00,2853
-16-Jul-17,84.90,70.00,0.00,2917
-17-Jul-17,84.90,73.90,0.00,3264
-18-Jul-17,87.10,75.90,0.00,3507
-19-Jul-17,91.00,77.00,0.00,3114
-20-Jul-17,93.00,78.10,0.01,2840
-21-Jul-17,91.00,77.00,0.00,2751
-22-Jul-17,91.00,78.10,0.57,2301
-23-Jul-17,78.10,73.00,0.06,2321
-24-Jul-17,69.10,63.00,0.74,1576
-25-Jul-17,71.10,64.00,0.00,3191
-26-Jul-17,75.90,66.00,0.00,3821
-27-Jul-17,77.00,66.90,0.01,3287
-28-Jul-17,84.90,73.00,0.00,3123
-29-Jul-17,75.90,68.00,0.00,2074
-30-Jul-17,81.00,64.90,0.00,3331
-31-Jul-17,88.00,66.90,0.00,3560
-1-Aug-17,91.00,72.00,0.00,3492
-2-Aug-17,86.00,69.10,0.09,2637
-3-Aug-17,86.00,70.00,0.00,3346
-4-Aug-17,82.90,70.00,0.15,2400
-5-Aug-17,77.00,70.00,0.30,3409
-6-Aug-17,75.90,64.00,0.00,3130
-7-Aug-17,71.10,64.90,0.76,804
-8-Aug-17,77.00,66.00,0.00,3598
-9-Aug-17,82.90,66.00,0.00,3893
-10-Aug-17,82.90,69.10,0.00,3423
-11-Aug-17,81.00,70.00,0.01,3148
-12-Aug-17,75.90,64.90,0.11,4146
-13-Aug-17,82.00,71.10,0.00,3274
-14-Aug-17,80.10,70.00,0.00,3291
-15-Aug-17,73.00,69.10,0.45,2149
-16-Aug-17,84.90,70.00,0.00,3685
-17-Aug-17,82.00,71.10,0.00,3637
-18-Aug-17,81.00,73.00,0.88,1064
-19-Aug-17,84.90,73.00,0.00,4693
-20-Aug-17,81.00,70.00,0.00,2822
-21-Aug-17,84.90,73.00,0.00,3088
-22-Aug-17,88.00,75.00,0.30,2983
-23-Aug-17,80.10,71.10,0.01,2994
-24-Aug-17,79.00,66.00,0.00,3688
-25-Aug-17,78.10,64.00,0.00,3144
-26-Aug-17,77.00,62.10,0.00,2710
-27-Aug-17,77.00,63.00,0.00,2676
-28-Aug-17,75.00,63.00,0.00,3332
-29-Aug-17,68.00,62.10,0.10,1472
-30-Aug-17,75.90,61.00,0.01,3468
-31-Aug-17,81.00,64.00,0.00,3279
-1-Sep-17,70.00,55.00,0.00,2945
-2-Sep-17,66.90,54.00,0.53,1876
-3-Sep-17,69.10,60.10,0.74,1004
-4-Sep-17,79.00,62.10,0.00,2866
-5-Sep-17,84.00,70.00,0.01,3244
-6-Sep-17,70.00,62.10,0.42,1232
-7-Sep-17,71.10,59.00,0.01,3249
-8-Sep-17,70.00,59.00,0.00,3234
-9-Sep-17,69.10,55.00,0.00,2609
-10-Sep-17,72.00,57.00,0.00,4960
-11-Sep-17,75.90,55.00,0.00,3657
-12-Sep-17,78.10,61.00,0.00,3497
-13-Sep-17,82.00,64.90,0.06,2994
-14-Sep-17,81.00,70.00,0.02,3013
-15-Sep-17,81.00,66.90,0.00,3344
-16-Sep-17,82.00,70.00,0.00,2560
-17-Sep-17,80.10,70.00,0.00,2676
-18-Sep-17,73.00,69.10,0.00,2673
-19-Sep-17,78.10,69.10,0.22,2012
-20-Sep-17,78.10,71.10,0.00,3296
-21-Sep-17,80.10,71.10,0.00,3317
-22-Sep-17,82.00,66.00,0.00,3297
-23-Sep-17,86.00,68.00,0.00,2810
-24-Sep-17,90.00,69.10,0.00,2543
-25-Sep-17,87.10,72.00,0.00,3276
-26-Sep-17,82.00,69.10,0.00,3157
-27-Sep-17,84.90,71.10,0.00,3216
-28-Sep-17,78.10,66.00,0.00,3421
-29-Sep-17,66.90,55.00,0.00,2988
-30-Sep-17,64.00,55.90,0.00,1903
-1-Oct-17,66.90,50.00,0.00,2297
-2-Oct-17,72.00,52.00,0.00,3387
-3-Oct-17,70.00,57.00,0.00,3386
-4-Oct-17,75.00,55.90,0.00,3412
-5-Oct-17,82.00,64.90,0.00,3312
-6-Oct-17,81.00,69.10,0.00,2982
-7-Oct-17,80.10,66.00,0.00,2750
-8-Oct-17,77.00,72.00,0.22,1235
-9-Oct-17,75.90,72.00,0.26,898
-10-Oct-17,80.10,66.00,0.00,3922
-11-Oct-17,75.00,64.90,0.06,2721
-12-Oct-17,63.00,55.90,0.07,2411
-13-Oct-17,64.90,52.00,0.00,2839
-14-Oct-17,71.10,62.10,0.08,2021
-15-Oct-17,72.00,66.00,0.01,2169
-16-Oct-17,60.10,52.00,0.01,2751
-17-Oct-17,57.90,43.00,0.00,2869
-18-Oct-17,71.10,50.00,0.00,3264
-19-Oct-17,70.00,55.90,0.00,3265
-20-Oct-17,73.00,57.90,0.00,3169
-21-Oct-17,78.10,57.00,0.00,2538
-22-Oct-17,75.90,57.00,0.00,2744
-23-Oct-17,73.90,64.00,0.00,3189
-24-Oct-17,73.00,66.90,0.20,954
-25-Oct-17,64.90,57.90,0.00,3367
-26-Oct-17,57.00,53.10,0.00,2565
-27-Oct-17,62.10,48.00,0.00,3150
-28-Oct-17,68.00,55.90,0.00,2245
-29-Oct-17,64.90,61.00,3.03,183
-30-Oct-17,55.00,46.00,0.25,1428
-31-Oct-17,54.00,44.00,0.00,2727

assignment-2/data/nyc_bb_bicyclist_counts.csv

@@ -1,215 +0,0 @@
-Date,HIGH_T,LOW_T,PRECIP,BB_COUNT
-1-Apr-17,46.00,37.00,0.00,606
-2-Apr-17,62.10,41.00,0.00,2021
-3-Apr-17,63.00,50.00,0.03,2470
-4-Apr-17,51.10,46.00,1.18,723
-5-Apr-17,63.00,46.00,0.00,2807
-6-Apr-17,48.90,41.00,0.73,461
-7-Apr-17,48.00,43.00,0.01,1222
-8-Apr-17,55.90,39.90,0.00,1674
-9-Apr-17,66.00,45.00,0.00,2375
-10-Apr-17,73.90,55.00,0.00,3324
-11-Apr-17,80.10,62.10,0.00,3887
-12-Apr-17,73.90,57.90,0.02,2565
-13-Apr-17,64.00,48.90,0.00,3353
-14-Apr-17,64.90,48.90,0.00,2942
-15-Apr-17,64.90,52.00,0.00,2253
-16-Apr-17,84.90,62.10,0.01,2877
-17-Apr-17,73.90,64.00,0.01,3152
-18-Apr-17,66.00,50.00,0.00,3415
-19-Apr-17,52.00,45.00,0.01,1965
-20-Apr-17,64.90,50.00,0.17,1567
-21-Apr-17,53.10,48.00,0.29,1426
-22-Apr-17,55.90,52.00,0.11,1318
-23-Apr-17,64.90,46.90,0.00,2520
-24-Apr-17,60.10,50.00,0.01,2544
-25-Apr-17,54.00,50.00,0.91,611
-26-Apr-17,59.00,54.00,0.34,1247
-27-Apr-17,68.00,59.00,0.00,2959
-28-Apr-17,82.90,57.90,0.00,3679
-29-Apr-17,84.00,64.00,0.06,3315
-30-Apr-17,64.00,54.00,0.00,2225
-1-May-17,72.00,50.00,0.00,3084
-2-May-17,73.90,66.90,0.00,3423
-3-May-17,64.90,57.90,0.00,3342
-4-May-17,63.00,50.00,0.00,3019
-5-May-17,59.00,52.00,3.02,513
-6-May-17,64.90,57.00,0.18,1892
-7-May-17,54.00,48.90,0.01,3539
-8-May-17,57.00,45.00,0.00,2886
-9-May-17,61.00,48.00,0.00,2718
-10-May-17,70.00,51.10,0.00,2810
-11-May-17,61.00,51.80,0.00,2657
-12-May-17,62.10,51.10,0.00,2640
-13-May-17,51.10,45.00,1.31,151
-14-May-17,64.90,46.00,0.02,1452
-15-May-17,66.90,55.90,0.00,2685
-16-May-17,78.10,57.90,0.00,3666
-17-May-17,90.00,66.00,0.00,3535
-18-May-17,91.90,75.00,0.00,3190
-19-May-17,90.00,75.90,0.00,2952
-20-May-17,64.00,55.90,0.01,2161
-21-May-17,66.90,55.00,0.00,2612
-22-May-17,61.00,54.00,0.59,768
-23-May-17,68.00,57.90,0.00,3174
-24-May-17,66.90,57.00,0.04,2969
-25-May-17,57.90,55.90,0.58,488
-26-May-17,73.00,55.90,0.10,2590
-27-May-17,71.10,61.00,0.00,2609
-28-May-17,71.10,59.00,0.00,2640
-29-May-17,57.90,55.90,0.13,836
-30-May-17,59.00,55.90,0.06,2301
-31-May-17,75.00,57.90,0.03,2689
-1-Jun-17,78.10,62.10,0.00,3468
-2-Jun-17,73.90,60.10,0.01,3271
-3-Jun-17,72.00,55.00,0.01,2589
-4-Jun-17,68.00,60.10,0.09,1805
-5-Jun-17,66.90,60.10,0.02,2171
-6-Jun-17,55.90,53.10,0.06,1193
-7-Jun-17,66.90,54.00,0.00,3211
-8-Jun-17,68.00,59.00,0.00,3253
-9-Jun-17,80.10,59.00,0.00,3401
-10-Jun-17,84.00,68.00,0.00,3066
-11-Jun-17,90.00,73.00,0.00,2465
-12-Jun-17,91.90,77.00,0.00,2854
-13-Jun-17,93.90,78.10,0.01,2882
-14-Jun-17,84.00,69.10,0.29,2596
-15-Jun-17,75.00,66.00,0.00,3510
-16-Jun-17,68.00,66.00,0.00,2054
-17-Jun-17,73.00,66.90,1.39,1399
-18-Jun-17,84.00,72.00,0.01,2199
-19-Jun-17,87.10,70.00,1.35,1648
-20-Jun-17,82.00,72.00,0.03,3407
-21-Jun-17,82.00,72.00,0.00,3304
-22-Jun-17,82.00,70.00,0.00,3368
-23-Jun-17,82.90,75.90,0.04,2283
-24-Jun-17,82.90,71.10,1.29,2307
-25-Jun-17,82.00,69.10,0.00,2625
-26-Jun-17,78.10,66.00,0.00,3386
-27-Jun-17,75.90,61.00,0.18,3182
-28-Jun-17,78.10,62.10,0.00,3766
-29-Jun-17,81.00,68.00,0.00,3356
-30-Jun-17,88.00,73.90,0.01,2687
-1-Jul-17,84.90,72.00,0.23,1848
-2-Jul-17,87.10,73.00,0.00,2467
-3-Jul-17,87.10,71.10,0.45,2714
-4-Jul-17,82.90,70.00,0.00,2296
-5-Jul-17,84.90,71.10,0.00,3170
-6-Jul-17,75.00,71.10,0.01,3065
-7-Jul-17,79.00,68.00,1.78,1513
-8-Jul-17,82.90,70.00,0.00,2718
-9-Jul-17,81.00,69.10,0.00,3048
-10-Jul-17,82.90,71.10,0.00,3506
-11-Jul-17,84.00,75.00,0.00,2929
-12-Jul-17,87.10,77.00,0.00,2860
-13-Jul-17,89.10,77.00,0.00,2563
-14-Jul-17,69.10,64.90,0.35,907
-15-Jul-17,82.90,68.00,0.00,2853
-16-Jul-17,84.90,70.00,0.00,2917
-17-Jul-17,84.90,73.90,0.00,3264
-18-Jul-17,87.10,75.90,0.00,3507
-19-Jul-17,91.00,77.00,0.00,3114
-20-Jul-17,93.00,78.10,0.01,2840
-21-Jul-17,91.00,77.00,0.00,2751
-22-Jul-17,91.00,78.10,0.57,2301
-23-Jul-17,78.10,73.00,0.06,2321
-24-Jul-17,69.10,63.00,0.74,1576
-25-Jul-17,71.10,64.00,0.00,3191
-26-Jul-17,75.90,66.00,0.00,3821
-27-Jul-17,77.00,66.90,0.01,3287
-28-Jul-17,84.90,73.00,0.00,3123
-29-Jul-17,75.90,68.00,0.00,2074
-30-Jul-17,81.00,64.90,0.00,3331
-31-Jul-17,88.00,66.90,0.00,3560
-1-Aug-17,91.00,72.00,0.00,3492
-2-Aug-17,86.00,69.10,0.09,2637
-3-Aug-17,86.00,70.00,0.00,3346
-4-Aug-17,82.90,70.00,0.15,2400
-5-Aug-17,77.00,70.00,0.30,3409
-6-Aug-17,75.90,64.00,0.00,3130
-7-Aug-17,71.10,64.90,0.76,804
-8-Aug-17,77.00,66.00,0.00,3598
-9-Aug-17,82.90,66.00,0.00,3893
-10-Aug-17,82.90,69.10,0.00,3423
-11-Aug-17,81.00,70.00,0.01,3148
-12-Aug-17,75.90,64.90,0.11,4146
-13-Aug-17,82.00,71.10,0.00,3274
-14-Aug-17,80.10,70.00,0.00,3291
-15-Aug-17,73.00,69.10,0.45,2149
-16-Aug-17,84.90,70.00,0.00,3685
-17-Aug-17,82.00,71.10,0.00,3637
-18-Aug-17,81.00,73.00,0.88,1064
-19-Aug-17,84.90,73.00,0.00,4693
-20-Aug-17,81.00,70.00,0.00,2822
-21-Aug-17,84.90,73.00,0.00,3088
-22-Aug-17,88.00,75.00,0.30,2983
-23-Aug-17,80.10,71.10,0.01,2994
-24-Aug-17,79.00,66.00,0.00,3688
-25-Aug-17,78.10,64.00,0.00,3144
-26-Aug-17,77.00,62.10,0.00,2710
-27-Aug-17,77.00,63.00,0.00,2676
-28-Aug-17,75.00,63.00,0.00,3332
-29-Aug-17,68.00,62.10,0.10,1472
-30-Aug-17,75.90,61.00,0.01,3468
-31-Aug-17,81.00,64.00,0.00,3279
-1-Sep-17,70.00,55.00,0.00,2945
-2-Sep-17,66.90,54.00,0.53,1876
-3-Sep-17,69.10,60.10,0.74,1004
-4-Sep-17,79.00,62.10,0.00,2866
-5-Sep-17,84.00,70.00,0.01,3244
-6-Sep-17,70.00,62.10,0.42,1232
-7-Sep-17,71.10,59.00,0.01,3249
-8-Sep-17,70.00,59.00,0.00,3234
-9-Sep-17,69.10,55.00,0.00,2609
-10-Sep-17,72.00,57.00,0.00,4960
-11-Sep-17,75.90,55.00,0.00,3657
-12-Sep-17,78.10,61.00,0.00,3497
-13-Sep-17,82.00,64.90,0.06,2994
-14-Sep-17,81.00,70.00,0.02,3013
-15-Sep-17,81.00,66.90,0.00,3344
-16-Sep-17,82.00,70.00,0.00,2560
-17-Sep-17,80.10,70.00,0.00,2676
-18-Sep-17,73.00,69.10,0.00,2673
-19-Sep-17,78.10,69.10,0.22,2012
-20-Sep-17,78.10,71.10,0.00,3296
-21-Sep-17,80.10,71.10,0.00,3317
-22-Sep-17,82.00,66.00,0.00,3297
-23-Sep-17,86.00,68.00,0.00,2810
-24-Sep-17,90.00,69.10,0.00,2543
-25-Sep-17,87.10,72.00,0.00,3276
-26-Sep-17,82.00,69.10,0.00,3157
-27-Sep-17,84.90,71.10,0.00,3216
-28-Sep-17,78.10,66.00,0.00,3421
-29-Sep-17,66.90,55.00,0.00,2988
-30-Sep-17,64.00,55.90,0.00,1903
-1-Oct-17,66.90,50.00,0.00,2297
-2-Oct-17,72.00,52.00,0.00,3387
-3-Oct-17,70.00,57.00,0.00,3386
-4-Oct-17,75.00,55.90,0.00,3412
-5-Oct-17,82.00,64.90,0.00,3312
-6-Oct-17,81.00,69.10,0.00,2982
-7-Oct-17,80.10,66.00,0.00,2750
-8-Oct-17,77.00,72.00,0.22,1235
-9-Oct-17,75.90,72.00,0.26,898
-10-Oct-17,80.10,66.00,0.00,3922
-11-Oct-17,75.00,64.90,0.06,2721
-12-Oct-17,63.00,55.90,0.07,2411
-13-Oct-17,64.90,52.00,0.00,2839
-14-Oct-17,71.10,62.10,0.08,2021
-15-Oct-17,72.00,66.00,0.01,2169
-16-Oct-17,60.10,52.00,0.01,2751
-17-Oct-17,57.90,43.00,0.00,2869
-18-Oct-17,71.10,50.00,0.00,3264
-19-Oct-17,70.00,55.90,0.00,3265
-20-Oct-17,73.00,57.90,0.00,3169
-21-Oct-17,78.10,57.00,0.00,2538
-22-Oct-17,75.90,57.00,0.00,2744
-23-Oct-17,73.90,64.00,0.00,3189
-24-Oct-17,73.00,66.90,0.20,954
-25-Oct-17,64.90,57.90,0.00,3367
-26-Oct-17,57.00,53.10,0.00,2565
-27-Oct-17,62.10,48.00,0.00,3150
-28-Oct-17,68.00,55.90,0.00,2245
-29-Oct-17,64.90,61.00,3.03,183
-30-Oct-17,55.00,46.00,0.25,1428
-31-Oct-17,54.00,44.00,0.00,2727

assignment-2/pyproject.toml

@@ -1,23 +0,0 @@
-[tool.poetry]
-name = "assignment-2"
-version = "0.1.0"
-description = ""
-authors = ["Artur Janik <aj2614@nyu.edu>"]
-readme = "README.md"
-packages = [{include = "assignment_2"}]
-
-[tool.poetry.dependencies]
-python = "^3.10"
-black = "^23.1.0"
-jupyterlab = "^3.6.1"
-ipython = "^8.10.0"
-numpy = "^1.24.2"
-pandas = "^1.5.3"
-jax = "^0.4.4"
-seaborn = "^0.12.2"
-matplotlib = "^3.7.0"
-
-
-[build-system]
-requires = ["poetry-core"]
-build-backend = "poetry.core.masonry.api"

language_models_project/app.py

@@ -0,0 +1,47 @@
+import easyocr as ocr  #OCR
+import streamlit as st  #Web App
+from PIL import Image #Image Processing
+import numpy as np #Image Processing 
+
+#title
+st.title("Easy OCR - Extract Text from Images")
+
+#subtitle
+st.markdown("## Optical Character Recognition - Using `easyocr`, `streamlit` -  hosted on 🤗 Spaces")
+
+st.markdown("Link to the app - [image-to-text-app on 🤗 Spaces](https://huggingface.co/spaces/Amrrs/image-to-text-app)")
+
+#image uploader
+image = st.file_uploader(label = "Upload your image here",type=['png','jpg','jpeg'])
+
+
+@st.cache
+def load_model(): 
+    reader = ocr.Reader(['en'],model_storage_directory='.')
+    return reader 
+
+reader = load_model() #load model
+
+if image is not None:
+
+    input_image = Image.open(image) #read image
+    st.image(input_image) #display image
+
+    with st.spinner("🤖 AI is at Work! "):
+        
+
+        result = reader.readtext(np.array(input_image))
+
+        result_text = [] #empty list for results
+
+
+        for text in result:
+            result_text.append(text[1])
+
+        st.write(result_text)
+    #st.success("Here you go!")
+    st.balloons()
+else:
+    st.write("Upload an Image")
+
+st.caption("Made with ❤️ by @1littlecoder. Credits to 🤗 Spaces for Hosting this ")

midterm/Pipfile

@@ -1,19 +0,0 @@
-[[source]]
-url = "https://pypi.org/simple"
-verify_ssl = true
-name = "pypi"
-
-[packages]
-black = "*"
-jupyterlab = "*"
-pandas = "*"
-scikit-learn = "*"
-numpy = "*"
-ipython = "*"
-seaborn = "*"
-imblearn = "*"
-
-[dev-packages]
-
-[requires]
-python_version = "3.10"

midterm/pyproject.toml

@@ -1,22 +0,0 @@
-[tool.poetry]
-name = "midterm"
-version = "0.1.0"
-description = ""
-authors = ["Your Name <you@example.com>"]
-readme = "README.md"
-
-[tool.poetry.dependencies]
-python = "^3.10"
-black = "^23.1.0"
-jupyterlab = "^3.6.1"
-ipython = "^8.10.0"
-numpy = "^1.24.2"
-pandas = "^1.5.3"
-jax = "^0.4.4"
-seaborn = "^0.12.2"
-matplotlib = "^3.7.0"
-
-
-[build-system]
-requires = ["poetry-core"]
-build-backend = "poetry.core.masonry.api"