from sklearn.linear_model import LinearRegression lin_reg = LinearRegression() lin_reg.fit(X, y) #Fitting polynomial regression to the dataset from sklearn.preprocessing import PolynomialFeatures poly_reg = PolynomialFeatures(degree = 4) X_poly = poly_reg.fit_transform(X) lin_reg2 = LinearRegression() lin_reg2.fit(X_poly, y) plt.scatter(X, y, color='red') plt.plot(X, lin_reg.predict(X), color='blue') plt.title('Truth of bluff (Linear regression)') plt.xlabel('Position level') plt.ylabel('Salary') plt.show() X_grid = np.arange(min(X), max(X), 0.1) X_grid = X_grid.reshape((len(X_grid), 1)) plt.scatter(X, y, color='red') plt.plot(X_grid, lin_reg2.predict(poly_reg.fit_transform(X_grid)), color='blue') plt.title('Truth of bluff (Linear regression)') plt.xlabel('Position level') plt.ylabel('Salary') plt.show() #Predicting a new result with linear regression lin_reg.predict(array.reshape(1, 6.5)) #Predicting a new result with polynomial regression
x = numpy.zeros(7, int) print(x) x = numpy.ones(7) print(x) arr = numpy.array([[3, 4, 5, 5], [6, 7, 8, 8], [5, 6, 7, 3]]) print(arr) arr2 = arr + 2 print(arr2) arr3 = arr + arr2 print(arr3) arr4 = arr.flatten() #multi dimensional to single dimensional print(arr4) arr.reshape(3, 2, 2) # to multi dimentionsl matrix arr = numpy.array([[3, 4, 5], [6, 7, 8], [5, 6, 7]]) print(arr) m1 = matrix(arr) #matxi print(m1) m1 = numpy.matrix('3,4,5;5,6,7;7,8,9') print(m1) m1.dtype print(diagonal(arr)) #######################MULTITHREADING###################### from threading import * from time import sleep class Hello(Thread): def run(self):
print(arr1 + arr2) #adds corresponding elemnets or arrays arr = arr1 + 5 #adds 5 to all elements of array print(arr) print(concatenate([arr1, arr2])) arr = arr1.view() #copies the array with changes made print(arr) arr1[1] = 19 print(arr) arr = arr1.copy() #deep copy arr1[1] = 10 print(arr) from numpy import * arr = array([[1, 2, 3, 4, 5, 6], [6, 7, 8, 9, 0, 1]]) print(arr.flatten()) #converts to one dimensional print(arr.reshape(2, 2, 3)) #convert array into 2*2*3 dimensions a = [ [1, 2, 3], #list as matrix and function for multiplication of matrix [4, 5, 6] ] b = [[10, 11, 12, 13], [12, 13, 14, 15], [14, 15, 16, 17]] def mml(x, y): if len(x[0]) == len(y): #check matrix conpatibility a = len(x) b = len(x[0]) c = len(y[0]) z = [[], []] sum = 0
def _sframe_to_nparray(sf): """ Converts a numeric SFrame to a numpy Array. Every column in the SFrame must be of numeric (integer, float) or array.array type. The resultant Numpy array the same number of rows as the input SFrame, and with each row merged into a single array. Missing values in integer columns are converted to 0, and missing values in float columns are converted to NaN. Example: >>> sf = gl.SFrame({'a':[1,1,None], >>> 'b':[2.0,2.0,None], >>> 'c':[[3.0,3.0],[3.0,3.0],[3.0,3.0]]}) >>> sf Columns: a int b float c array Rows: 3 Data: +------+------+------------+ | a | b | c | +------+------+------------+ | 1 | 2.0 | [3.0, 3.0] | | 1 | 2.0 | [3.0, 3.0] | | None | None | [3.0, 3.0] | +------+------+------------+ [3 rows x 3 columns] >>> n = gl.numpy.sframe_to_nparray(sf) >>> n array([[ 1., 2., 3., 3.], [ 1., 2., 3., 3.], [ 0., nan, 3., 3.]]) """ _mt._get_metric_tracker().track('snumpy.sframe_to_np') if not numpy_activation_successful(): raise RuntimeError( "This function cannot be used if Scalable Numpy activation failed") import graphlab import graphlab.util import ctypes import numpy as np import graphlab.cython.pointer_to_ndarray import array if not all(d in [int, float, array.array] for d in sf.dtype()): raise TypeError( "Only integer, float or array column types are supported") temp_dir = graphlab.util._make_temp_directory("numpy_convert_") # only sframes have save_reference sf._save_reference(temp_dir) unity_dll = _get_unity_dll() if unity_dll == None: raise RuntimeError("fast Converter not loaded") if all(d in [int] for d in sf.dtype()): ptr = unity_dll.pointer_from_sframe(temp_dir.encode(), True) if not ptr: raise RuntimeError("Unable to convert to numpy array") arrlen = unity_dll.pointer_length(ptr) // 8 ArrayType = ctypes.c_int64 * arrlen addr = ctypes.addressof(ptr.contents) array = np.frombuffer(ArrayType.from_address(addr), np.int64) graphlab.cython.pointer_to_ndarray.numpy_own_array(array) width = arrlen / len(sf) if width > 1: return array.reshape(len(sf), width) else: return array else: unity_dll.pointer_from_sframe.restype = ctypes.POINTER(ctypes.c_double) ptr = unity_dll.pointer_from_sframe(temp_dir.encode(), True) if not ptr: raise RuntimeError("Unable to convert to numpy array") arrlen = unity_dll.pointer_length(ptr) // 8 ArrayType = ctypes.c_double * arrlen addr = ctypes.addressof(ptr.contents) array = np.frombuffer(ArrayType.from_address(addr), np.double) graphlab.cython.pointer_to_ndarray.numpy_own_array(array) width = arrlen / len(sf) if width > 1: return array.reshape(len(sf), arrlen / len(sf)) else: return array
def _sframe_to_nparray(sf): """ Converts a numeric SFrame to a numpy Array. Every column in the SFrame must be of numeric (integer, float) or array.array type. The resultant Numpy array the same number of rows as the input SFrame, and with each row merged into a single array. Missing values in integer columns are converted to 0, and missing values in float columns are converted to NaN. Example: >>> sf = gl.SFrame({'a':[1,1,None], >>> 'b':[2.0,2.0,None], >>> 'c':[[3.0,3.0],[3.0,3.0],[3.0,3.0]]}) >>> sf Columns: a int b float c array Rows: 3 Data: +------+------+------------+ | a | b | c | +------+------+------------+ | 1 | 2.0 | [3.0, 3.0] | | 1 | 2.0 | [3.0, 3.0] | | None | None | [3.0, 3.0] | +------+------+------------+ [3 rows x 3 columns] >>> n = gl.numpy.sframe_to_nparray(sf) >>> n array([[ 1., 2., 3., 3.], [ 1., 2., 3., 3.], [ 0., nan, 3., 3.]]) """ _mt._get_metric_tracker().track('snumpy.sframe_to_np') if not numpy_activation_successful(): raise RuntimeError("This function cannot be used if Scalable Numpy activation failed") import graphlab import graphlab.util import ctypes import numpy as np import graphlab.cython.pointer_to_ndarray import array if not all(d in [int, float, array.array] for d in sf.dtype()): raise TypeError("Only integer, float or array column types are supported") temp_dir = graphlab.util._make_temp_directory("numpy_convert_") # only sframes have save_reference sf._save_reference(temp_dir) unity_dll = _get_unity_dll() if unity_dll == None: raise RuntimeError("fast Converter not loaded") if all(d in [int] for d in sf.dtype()): ptr = unity_dll.pointer_from_sframe(temp_dir, True) if not ptr: raise RuntimeError("Unable to convert to numpy array") arrlen = unity_dll.pointer_length(ptr) / 8 ArrayType = ctypes.c_int64 * arrlen addr = ctypes.addressof(ptr.contents) array = np.frombuffer(ArrayType.from_address(addr), np.int64) graphlab.cython.pointer_to_ndarray.numpy_own_array(array) width = arrlen / len(sf) if width > 1: return array.reshape(len(sf), width) else: return array else: unity_dll.pointer_from_sframe.restype = ctypes.POINTER(ctypes.c_double) ptr = unity_dll.pointer_from_sframe(temp_dir, True) if not ptr: raise RuntimeError("Unable to convert to numpy array") arrlen = unity_dll.pointer_length(ptr) / 8 ArrayType = ctypes.c_double * arrlen addr = ctypes.addressof(ptr.contents) array = np.frombuffer(ArrayType.from_address(addr), np.double) graphlab.cython.pointer_to_ndarray.numpy_own_array(array) width = arrlen / len(sf) if width > 1: return array.reshape(len(sf), arrlen / len(sf)) else: return array
def get_image(self, array): img = array.reshape((28, 28)) img = Image.fromarray(img) return img