From 96a983a7984cb79256a25a6e258c5fadc804db1d Mon Sep 17 00:00:00 2001
From: Fortune Uwha <fortune.uwha@gmail.com>
Date: Wed, 22 Apr 2020 22:57:22 +0100
Subject: [PATCH 1/6] Delete DSN_logo.png

---
 DSN_logo.png | Bin 30700 -> 0 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 DSN_logo.png

diff --git a/DSN_logo.png b/DSN_logo.png
deleted file mode 100644
index b88c2bc9671b13503dff282b7c57ce37d26dacd6..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

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From bb15d24cde77f1ac257d10aebb43914e3a09c58c Mon Sep 17 00:00:00 2001
From: Fortune Uwha <fortune.uwha@gmail.com>
Date: Thu, 23 Apr 2020 00:05:51 +0100
Subject: [PATCH 2/6] Create Uwha_Fortune_Oluchi

---
 Uwha_Fortune_Oluchi | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 Uwha_Fortune_Oluchi

diff --git a/Uwha_Fortune_Oluchi b/Uwha_Fortune_Oluchi
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/Uwha_Fortune_Oluchi
@@ -0,0 +1 @@
+

From 8a4fc7fb0182651938dc2dac8b068c4a5e9e1059 Mon Sep 17 00:00:00 2001
From: Fortune Uwha <fortune.uwha@gmail.com>
Date: Thu, 23 Apr 2020 00:19:21 +0100
Subject: [PATCH 3/6] Update README.md

---
 README.md | 40 ++++------------------------------------
 1 file changed, 4 insertions(+), 36 deletions(-)

diff --git a/README.md b/README.md
index d3696d5..8cc1285 100644
--- a/README.md
+++ b/README.md
@@ -1,41 +1,9 @@
 # ML-Logistic-regression-algorithm-challenge
 
+In this notebook I will try to implement a Logistic Regression without relying to Python's easy-to-use scikit-learn library. This notebook aims to create a Logistic Regression without the help of in-built Logistic Regression libraries to help us fully understand how Logistic Regression works in the background.
 
-![DSN logo](DSN_logo.png)|DSN Algorithm Challenge|
-|---|---|
+Before we start coding let us first understand or atleast try to understand the things happening at the back-end of Logistic Regression. Logistic regression is a classiication algorithm, it is maybe sometimes be confusing that the term regression appears in this name even though logistic regression is actually a classiication algorithm. But that's just a name for historical reasons.So let's not get confused, logistic regression is actually a classiication algorithm that we apply to settings where the label y is a discrete value, when it's either zero or one
 
-A lot of data scientists or machine learning enthusiasts do use various machine learning algorithms as a black box without knowing how they work or the mathematics behind it. The purpose of this challenge is to encourage the mathematical understanding of machine learning algorithms, their break and yield point. 
+A Logistic regression takes input and returns an output of probability, a value between 0 and 1. How does a Logistic Regression do that? With the help of a function called a logistic function or most commonly known as a sigmoid. The terms sigmoid function and logistic function are basically synonyms and mean the same thing. So the two terms are basically interchangable, and either term can be used to refer to this function. 
 
-In summary, participants are encouraged to understand the fundamental concepts behind machine learning algorithms/models.
-
-
-The rules and guidelines for this challenge are as follows:
-
-1. Ensure to register at https://bit.ly/dsnmlhack 
-
-2. The algorithm challenge is open to all.
-
-3. Participants are expected to design and develop the Logistic Regression algorithm from scratch using Python or R programming.
-
-4. For python developers (numpy is advisable).
-
-5. To push your solution to us, make a [pull request](https://help.github.com/en/github/collaborating-with-issues-and-pull-requests/about-pull-requests) to DSN's GitHub page at  https://www.github.com/datasciencenigeria/ML-Logistic-regression-algorithm-challenge. Ensure to add your readme file to understand your code.
-
-6. The top 3 optimized code will be compensated as follows:
-
-- **1st position**: 20GB data plan.
-- **2nd position**: 15GB data plan.
-- **3rd position**: 10GB data plan.
-
-7. Add your scripts and readme.MD file as a folder saved as your full name (surname_first_middle name) by making a pull request to the repository.
-
----
-For issues on this challenge kindly reach out to the AI+campus/city managers
-
-**Twitter**: [@DataScienceNIG](https://twitter.com/DataScienceNIG), [@elishatofunmi](https://twitter.com/Elishatofunmi), [@o_funminiyi](https://twitter.com/o_funminiyi), [@gbganalyst](https://twitter.com/gbganalyst) 
-
-or
-
-**Call**: +2349062000119,+2349080564419.
-
-Good luck!
+The sigmoid function also called the logistic function gives an ‘S’ shaped curve that can take any real-valued number and map it into a value between 0 and 1. If the curve goes to positive infinity, y predicted will become 1, and if the curve goes to negative infinity, y predicted will become 0. If the output of the sigmoid function is more than 0.5, we can classify the outcome as 1 or YES, and if it is less than 0.5, we can classify it like 0 or NO.

From 790082893e409a50f39a5cf7f0ffdb843157190c Mon Sep 17 00:00:00 2001
From: Fortune Uwha <fortune.uwha@gmail.com>
Date: Thu, 23 Apr 2020 00:25:59 +0100
Subject: [PATCH 4/6] Add files via upload

---
 ... Regression from scratch using numpy.ipynb | 131 ++++++++++++++++++
 1 file changed, 131 insertions(+)
 create mode 100644 DSN Logistic Regression from scratch using numpy.ipynb

diff --git a/DSN Logistic Regression from scratch using numpy.ipynb b/DSN Logistic Regression from scratch using numpy.ipynb
new file mode 100644
index 0000000..51c2d8b
--- /dev/null
+++ b/DSN Logistic Regression from scratch using numpy.ipynb	
@@ -0,0 +1,131 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Class Implementation for Logistic Regression: Binary logistic Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class logisticRegression:\n",
+    "    \n",
+    "    def __init__(self,lr=0.001,n_iters = 1000):\n",
+    "        self.lr = lr\n",
+    "        self.n_iters = n_iters\n",
+    "        self.weights = None\n",
+    "        self.bias = None\n",
+    "        \n",
+    "    def _sigmoid(self,x):\n",
+    "        return 1/(1+np.exp(-x))    \n",
+    "        \n",
+    "    def fit(self,X,y):\n",
+    "        #Initialize parameters\n",
+    "        n_samples,n_features = X.shape\n",
+    "        self.weights = np.zeros(n_features)\n",
+    "        self.bias=0 # you can also use random numbers but zero is fine\n",
+    "        #gradient descent\n",
+    "        for _ in range(self.n_iters):\n",
+    "            linear_model = np.dot(X,self.weights)+self.bias\n",
+    "            # Applying the sigmoid function\n",
+    "            y_predicted = self._sigmoid(linear_model)# So this is our approximation of y\n",
+    "            # Next, we need to update our weight\n",
+    "            dw = (1/n_samples)*np.dot(X.T,(y_predicted-y))\n",
+    "            db = (1/n_samples)*np.sum(y_predicted-y)\n",
+    "            #update our parameters\n",
+    "            self.weights -= self.lr*dw\n",
+    "            self.bias -=self.lr*db\n",
+    "    #Implement the predict method, we first approximate our data with a linear model,and apply the sigmoid function to get a probability\n",
+    "    def predict(self,X):\n",
+    "            linear_model = np.dot(X,self.weights)+self.bias\n",
+    "            y_predicted = self._sigmoid(linear_model)\n",
+    "            y_predicted_cls = [1 if i>0.5 else 0 for i in y_predicted]\n",
+    "            return y_predicted_cls\n",
+    "    def accuracy (y_true,y_pred):\n",
+    "        accuracy = np.sum(y_true == y_pred)/len(y_true)\n",
+    "        return accuracy   "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Class Implementation for Logistic Regression: Multiclass logistic Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "class MultiClassLogisticRegression:\n",
+    "    \n",
+    "    def fit(self, X, y, lr=0.00001, n_iter=1000):\n",
+    "        X = np.insert(X, 0, 1, axis=1)\n",
+    "        self.unique_y = np.unique(y)\n",
+    "        self.w = np.zeros[(len(self.unique_y), X.shape[1])]\n",
+    "        #so we are converting our y into the same shape as predictions using one-hot encoding\n",
+    "        y = self.one_hot(y)\n",
+    "        for i in range(n_iter):\n",
+    "            predictions = self.probabilities(X)\n",
+    "            #update weight\n",
+    "            error = predictions - y\n",
+    "            gradient = np.dot(error.T, X)\n",
+    "            self.w -= (lr * gradient)     \n",
+    "        return self\n",
+    "    \n",
+    "    def probabilities(self, X):\n",
+    "        scores = np.dot(X, self.w.T)\n",
+    "        return self.softmax(scores)\n",
+    "    \n",
+    "    def softmax(self, z):\n",
+    "        return np.exp(z)/ np.sum(np.exp(z), axis=1).reshape[-1,1]\n",
+    "        \n",
+    "    def predict(self, X):\n",
+    "        X = np.insert(X, 0, 1, axis=1)\n",
+    "        #we use the np.vectorize to convert our predicted classes to actual classes that we have in y\n",
+    "        return np.vectorize(lambda i: self.unique_y[i])(np.argmax(self.probabilities(X), axis = 1))# returning the index of the highest probability for each row in X\n",
+    "    \n",
+    "    def score(self, X, y):\n",
+    "        return np.mean(self.predict(X) == y)\n",
+    "    \n",
+    "    def one_hot(self, y):\n",
+    "        u_y = list(np.unique(y))\n",
+    "        encoded = np.zeros((len[y], len(u_y)))\n",
+    "        for i, c in enumerate[y]:\n",
+    "            encoded[i][u_y.index(c)] = 1\n",
+    "        return encoded\n",
+    "    "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 38763260fc811bf6a8d8c791346882553ac668b6 Mon Sep 17 00:00:00 2001
From: Fortune Uwha <fortune.uwha@gmail.com>
Date: Wed, 10 Jun 2020 12:12:06 +0100
Subject: [PATCH 5/6] Delete Uwha_Fortune_Oluchi

---
 Uwha_Fortune_Oluchi | 1 -
 1 file changed, 1 deletion(-)
 delete mode 100644 Uwha_Fortune_Oluchi

diff --git a/Uwha_Fortune_Oluchi b/Uwha_Fortune_Oluchi
deleted file mode 100644
index 8b13789..0000000
--- a/Uwha_Fortune_Oluchi
+++ /dev/null
@@ -1 +0,0 @@
-

From 25d7d294aecc189a4cc02dfa6a58d84927847f74 Mon Sep 17 00:00:00 2001
From: Fortune Uwha <fortune.uwha@gmail.com>
Date: Wed, 10 Jun 2020 12:15:35 +0100
Subject: [PATCH 6/6] Update README.md

---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 8cc1285..4aaf6a0 100644
--- a/README.md
+++ b/README.md
@@ -2,8 +2,8 @@
 
 In this notebook I will try to implement a Logistic Regression without relying to Python's easy-to-use scikit-learn library. This notebook aims to create a Logistic Regression without the help of in-built Logistic Regression libraries to help us fully understand how Logistic Regression works in the background.
 
-Before we start coding let us first understand or atleast try to understand the things happening at the back-end of Logistic Regression. Logistic regression is a classiication algorithm, it is maybe sometimes be confusing that the term regression appears in this name even though logistic regression is actually a classiication algorithm. But that's just a name for historical reasons.So let's not get confused, logistic regression is actually a classiication algorithm that we apply to settings where the label y is a discrete value, when it's either zero or one
+Before we start coding let us first understand or atleast try to understand the things happening at the back-end of Logistic Regression. Logistic regression is a classification algorithm, it is sometimes confusing that the term regression appears in this name even though logistic regression is actually a classification algorithm. But that's just a name for historical reasons.So let's not get confused, logistic regression is actually a classiication algorithm that we apply to settings where the label y is a discrete value, when it's either zero or one
 
 A Logistic regression takes input and returns an output of probability, a value between 0 and 1. How does a Logistic Regression do that? With the help of a function called a logistic function or most commonly known as a sigmoid. The terms sigmoid function and logistic function are basically synonyms and mean the same thing. So the two terms are basically interchangable, and either term can be used to refer to this function. 
 
-The sigmoid function also called the logistic function gives an ‘S’ shaped curve that can take any real-valued number and map it into a value between 0 and 1. If the curve goes to positive infinity, y predicted will become 1, and if the curve goes to negative infinity, y predicted will become 0. If the output of the sigmoid function is more than 0.5, we can classify the outcome as 1 or YES, and if it is less than 0.5, we can classify it like 0 or NO.
+The sigmoid function also called the logistic function, gives an ‘S’ shaped curve that can take any real-valued number and map it into a value between 0 and 1. If the curve goes to positive infinity, y predicted will become 1, and if the curve goes to negative infinity, y predicted will become 0. If the output of the sigmoid function is more than 0.5, we can classify the outcome as 1 or YES, and if it is less than 0.5, we can classify it like 0 or NO.