Jargon4072

Jargon4072

def run_lr(res,data):
    # Note: data should be given in pandas format, as shwon below:
    '''
    Brain_weight	Body_weight
    62.00	         1320.0
    55.50	         175.0
    35.00	         56.0
    52.16	         440.0
    0.28	         1.9
    '''

Jargon4072

Jargon4072

def step_gradient(b_current, m_current, points, learningRate):
    b_gradient = 0
    m_gradient = 0
    N = float(len(points))
    for i in range(0, len(points)):
        x = points[i, 0]
        y = points[i, 1]
        b_gradient += -(2/N) * (y - ((m_current * x) + b_current))
        m_gradient += -(2/N) * x * (y - ((m_current * x) + b_current))
    new_b = b_current - (learningRate * b_gradient)

Jargon4072

from numpy import *

# y = mx + b
# m is slope, b is y-intercept
# point is tuple of x,y value at a given step with data as points[i]= [xi, yi]
def compute_error_for_line_given_points(b, m, points):
    totalError = 0
    for i in range(0, len(points)):
        x = points[i, 0]
        y = points[i, 1]

Jargon4072

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd

df1=pd.read_csv("./x01.csv")
df1.columns=["Index","Brain_weight","Body_weight"]

corr_data = df1.corr()    #we are using pandas library's corr() function to find correlation.
print(corr_data)
plt.figure(figsize=(9,5))

Jargon4072

# @Author: Dwivedi Chandan
# @Date:   2019-08-05T13:35:05+05:30
# @Email:  chandandwivedi795@gmail.com
# @Last modified by:   Dwivedi Chandan
# @Last modified time: 2019-08-07T11:52:45+05:30


# import the necessary packages
import numpy as np
import argparse

Jargon4072

def main():
    # load our input image and grab its spatial dimensions
    image = cv2.imread("./test1.jpg")
    labelsPath="yolo_v3/coco.names"
    cfgpath="yolo_v3/yolov3.cfg"
    wpath="yolo_v3/yolov3.weights"
    Lables=get_labels(labelsPath)
    CFG=get_config(cfgpath)
    Weights=get_weights(wpath)
    nets=load_model(CFG,Weights)

Jargon4072

def get_predection(image,net,LABELS,COLORS):
    (H, W) = image.shape[:2]

    # determine only the *output* layer names that we need from YOLO
    ln = net.getLayerNames()
    ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]

    # construct a blob from the input image and then perform a forward
    # pass of the YOLO object detector, giving us our bounding boxes and
    # associated probabilities

Jargon4072

def get_labels(labels_path):
    # load the COCO class labels our YOLO model was trained on
    #labelsPath = os.path.sep.join([yolo_path, "yolo_v3/coco.names"])
    lpath=os.path.sep.join([yolo_path, labels_path])
    LABELS = open(lpath).read().strip().split("\n")
    return LABELS

def get_colors(LABELS):
    # initialize a list of colors to represent each possible class label
    np.random.seed(42)